Cognitive Abilities Are Associated with Rapid Dynamics of Electrophysiological Connectome States

Overview

Journal Netw Neurosci

Publisher MIT Press

Specialty Neurology

Date 2024 Dec 30

PMID 39735509

Authors

Suhnyoung Jun

Stephen M Malone

Thomas H Alderson

Jeremy Harper

Ruskin H Hunt

Kathleen M Thomas

Sylia Wilson

William G Iacono

Sepideh Sadaghiani

Affiliations

Soon will be listed here.

Abstract

Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, hold significant implications for cognition. However, connectome dynamics at fast (>1 Hz) timescales highly relevant to cognition are poorly understood due to the dominance of inherently slow fMRI in connectome studies. Here, we investigated the behavioral significance of rapid electrophysiological connectome dynamics using source-localized EEG connectomes during resting state ( = 926, 473 females). We focused on dynamic connectome features pertinent to individual differences, specifically those with established heritability: Fractional Occupancy (i.e., the overall duration spent in each recurrent connectome state) in beta and gamma bands and Transition Probability (i.e., the frequency of state switches) in theta, alpha, beta, and gamma bands. Canonical correlation analysis found a significant relationship between the heritable phenotypes of subsecond connectome dynamics and cognition. Specifically, principal components of Transition Probabilities in alpha (followed by theta and gamma bands) and a cognitive factor representing visuospatial processing (followed by verbal and auditory working memory) most notably contributed to the relationship. We conclude that rapid connectome state transitions shape individuals' cognitive abilities and traits. Such subsecond connectome dynamics may inform about behavioral function and dysfunction and serve as endophenotypes for cognitive abilities.

References

Mathewson K, Prudhomme C, Fabiani M, Beck D, Lleras A, Gratton G . Making waves in the stream of consciousness: entraining oscillations in EEG alpha and fluctuations in visual awareness with rhythmic visual stimulation. J Cogn Neurosci. 2012; 24(12):2321-33. DOI: 10.1162/jocn_a_00288. View

Kim K, Duc N, Choi M, Lee B . EEG microstate features according to performance on a mental arithmetic task. Sci Rep. 2021; 11(1):343. PMC: 7801706. DOI: 10.1038/s41598-020-79423-7. View

Palva S, Palva J . Functional roles of alpha-band phase synchronization in local and large-scale cortical networks. Front Psychol. 2011; 2:204. PMC: 3166799. DOI: 10.3389/fpsyg.2011.00204. View

Jun S, Alderson T, Altmann A, Sadaghiani S . Dynamic trajectories of connectome state transitions are heritable. Neuroimage. 2022; 256:119274. PMC: 9223440. DOI: 10.1016/j.neuroimage.2022.119274. View

Keyes M, Malone S, Elkins I, Legrand L, McGue M, Iacono W . The enrichment study of the Minnesota twin family study: increasing the yield of twin families at high risk for externalizing psychopathology. Twin Res Hum Genet. 2009; 12(5):489-501. PMC: 2760025. DOI: 10.1375/twin.12.5.489. View

Desikan R, Segonne F, Fischl B, Quinn B, Dickerson B, Blacker D . An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006; 31(3):968-80. DOI: 10.1016/j.neuroimage.2006.01.021. View

Eichenbaum A, Pappas I, Lurie D, Cohen J, DEsposito M . Differential contributions of static and time-varying functional connectivity to human behavior. Netw Neurosci. 2021; 5(1):145-165. PMC: 7935045. DOI: 10.1162/netn_a_00172. View

Shine J, Koyejo O, Poldrack R . Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attention. Proc Natl Acad Sci U S A. 2016; 113(35):9888-91. PMC: 5024627. DOI: 10.1073/pnas.1604898113. View

Quinn A, Vidaurre D, Abeysuriya R, Becker R, Nobre A, Woolrich M . Task-Evoked Dynamic Network Analysis Through Hidden Markov Modeling. Front Neurosci. 2018; 12:603. PMC: 6121015. DOI: 10.3389/fnins.2018.00603. View

10.

Brookes M, Woolrich M, Luckhoo H, Price D, Hale J, Stephenson M . Investigating the electrophysiological basis of resting state networks using magnetoencephalography. Proc Natl Acad Sci U S A. 2011; 108(40):16783-8. PMC: 3189080. DOI: 10.1073/pnas.1112685108. View

11.

Deco G, Jirsa V, McIntosh A . Resting brains never rest: computational insights into potential cognitive architectures. Trends Neurosci. 2013; 36(5):268-74. DOI: 10.1016/j.tins.2013.03.001. View

12.

Hatz F, Hardmeier M, Benz N, Ehrensperger M, Gschwandtner U, Ruegg S . Microstate connectivity alterations in patients with early Alzheimer's disease. Alzheimers Res Ther. 2016; 7:78. PMC: 4697314. DOI: 10.1186/s13195-015-0163-9. View

13.

Raichle M, Gusnard D . Intrinsic brain activity sets the stage for expression of motivated behavior. J Comp Neurol. 2005; 493(1):167-76. DOI: 10.1002/cne.20752. View

14.

Coquelet N, De Tiege X, Roshchupkina L, Peigneux P, Goldman S, Woolrich M . Microstates and power envelope hidden Markov modeling probe bursting brain activity at different timescales. Neuroimage. 2021; 247:118850. PMC: 8803543. DOI: 10.1016/j.neuroimage.2021.118850. View

15.

Vidaurre D, Smith S, Woolrich M . Brain network dynamics are hierarchically organized in time. Proc Natl Acad Sci U S A. 2017; 114(48):12827-12832. PMC: 5715736. DOI: 10.1073/pnas.1705120114. View

16.

Ramos da Cruz J, Favrod O, Roinishvili M, Chkonia E, Brand A, Mohr C . EEG microstates are a candidate endophenotype for schizophrenia. Nat Commun. 2020; 11(1):3089. PMC: 7303216. DOI: 10.1038/s41467-020-16914-1. View

17.

Baker A, Brookes M, Rezek I, Smith S, Behrens T, Probert Smith P . Fast transient networks in spontaneous human brain activity. Elife. 2014; 3:e01867. PMC: 3965210. DOI: 10.7554/eLife.01867. View

18.

Tadel F, Baillet S, Mosher J, Pantazis D, Leahy R . Brainstorm: a user-friendly application for MEG/EEG analysis. Comput Intell Neurosci. 2011; 2011:879716. PMC: 3090754. DOI: 10.1155/2011/879716. View

19.

Dyer E, Kording K . Why the simplest explanation isn't always the best. Proc Natl Acad Sci U S A. 2023; 120(52):e2319169120. PMC: 10756184. DOI: 10.1073/pnas.2319169120. View

20.

Smith S, Nichols T, Vidaurre D, Winkler A, Behrens T, Glasser M . A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nat Neurosci. 2015; 18(11):1565-7. PMC: 4625579. DOI: 10.1038/nn.4125. View