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Cognitive Neurodynamics

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21-11-2019 | Research Article

The dynamic properties of a brain network during working memory based on the algorithm of cross-frequency coupling

Authors: Wei Zhang, Lei Guo, Dongzhao Liu, Guizhi Xu

Published in: Cognitive Neurodynamics | Issue 2/2020

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Abstract

Working memory (WM) refers to a memory system with limited energy for short-term maintenance and plays an important role in cognitive functions. At present, research regarding WM mostly focuses on the coordination between neural signals in the signal microelectrode channel. However, how neural signals coordinate the coding of WM at the network level is rarely studied. Cross-frequency coupling (CFC) reflects the coordinated effect between different frequency components (e.g., theta and gamma) of local field potentials (LFPs) during WM. In this study, we try to map the changes that occur in the brain networks during WM at the level of CFC between theta-gamma of LFPs. First, a 16-channel brain network by using the CFC between theta-gamma of LFPs during WM was constructed. Then, the dynamic properties of the brain network during WM were analyzed based on graph theory. Experimental results show that the LFPs power increased at the WM state than at resting stat, but decreased across learning; the CFC between theta-gamma increased with learning days and phase-amplitude coupling (PAC) in the WM state was higher than that in free choice state and rest state; the changes of average degree, average shortest path length and global efficiency had significant difference on learning days. We can indicate that the CFC between theta-gamma in the network plays an important role in the WM formation. Furthermore, correct storage of WM information will not change local information transmission and the small-world attribute, while, it can increase the network connection and efficiency of information transmission.

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Albouy P, Weiss A, Baillet S et al (2017) Selective entrainment of theta oscillations in the dorsal stream causally enhances auditory working memory performance. Neuron 94(1):193. https://doi.org/10.1016/j.neuron.2017.03.015 CrossRefPubMed

Antonakakis M, Dimitriadis SI, Zervakis M et al (2016) Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury. Int J Psychophysiol 102:1–11. https://doi.org/10.1016/j.ijpsycho.2016.02.002 CrossRefPubMed

Ateş FE, Cangöz B, Baskak B et al (2017) Frontal activity during a verbal emotional working memory task in patients with Alzheimer’s disease: a functional near-infrared spectroscopy study. Psychiatry Res Neuroimaging 261:29–34. https://doi.org/10.1016/j.pscychresns.2016.12.013 CrossRefPubMed

Bai W, Yi H, Liu T et al (2014) Incoordination between spikes and LFPs in Aβ1-42-mediated memory deficits in rats. Front Behav Neurosci 8:411. https://doi.org/10.3389/fnbeh.2014.00411 CrossRefPubMedPubMedCentral

Bittner RA, Linden DEJ, Roebroeck A et al (2015) The when and where of working memory dysfunction in early-onset schizophrenia-A functional magnetic resonance imaging study. Cereb Cortex 25(9):2494–2506. https://doi.org/10.1093/cercor/bhu050 CrossRefPubMed

Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186. https://doi.org/10.1038/nrn2575 CrossRefPubMed

Carver FW, Rubinstein DY, Gerlich AH et al (2019) Prefrontal high gamma during a magnetoencephalographic working memory task. Hum Brain Mapp 40(6):1774–1785. https://doi.org/10.1002/hbm.24489 CrossRefPubMed

Chen H, Lei H, Xu Q (2018) Neuronal activity pattern defects in the striatum in awake mouse model of Parkinson’s disease. Behav Brain Res 341:135–145. https://doi.org/10.1016/j.bbr.2017.12.018 CrossRefPubMed

Christophel TB, Klink PC, Spitzer B et al (2017) The distributed nature of working memory. Trends Cognit Sci 21(2):111. https://doi.org/10.1016/j.tics.2016.12.007 CrossRef

Dai Z, Yan C, Li K et al (2014) Identifying and mapping connectivity patterns of brain network hubs in Alzheimer’s disease. Cereb Cortex 25(10):3723. https://doi.org/10.1093/cercor/bhu246 CrossRefPubMed

Davis KE, Burnett K, Gigg J (2017) Water and T-maze protocols are equally efficient methods to assess spatial memory in 3xTg Alzheimer’s disease mice. Behav Brain Res 331:54. https://doi.org/10.1016/j.bbr.2017.05.005 CrossRefPubMed

Decoteau WE, Thorn C, Gibson DJ et al (2007) Oscillations of local field potentials in the rat dorsal striatum during spontaneous and instructed behaviors. J Neurophysiol 97(5):3800–3805. https://doi.org/10.1152/jn.00108.2007 CrossRefPubMed

Esmaeili V, Diamond ME (2019) Neuronal correlates of tactile working memory in prefrontal and vibrissal somatosensory cortex. Cell Rep 27(11):3167–3181. https://doi.org/10.1016/j.celrep.2019.05.034 CrossRefPubMedPubMedCentral

Funahashi S (2017) Working memory in the prefrontal cortex. Brain Sci 7(5):49. https://doi.org/10.3390/brainsci7050049 CrossRefPubMedCentral

Graetz S, Daume G, Friese U et al (2019) Alterations in oscillatory cortical activity indicate changes in mnemonic processing during continuous item recognition. Exp Brain Res 237(2):573–583. https://doi.org/10.1007/s00221-018-5439-4 CrossRefPubMed

Haj ME, Antoine P (2017) Describe yourself to improve your autobiographical memory: a study in Alzheimer’s disease. Cortex 88:165–172. https://doi.org/10.1016/j.cortex.2017.01.004 CrossRefPubMed

Jafakesh S, Jahromy FZ, Daliri MR (2016) Decoding of object categories from brain signals using cross frequency coupling methods. Biomed Signal Process 27:60–67. https://doi.org/10.1016/j.bspc.2016.01.013 CrossRef

Li S, Ouyang M, Liu T et al (2014) Increase of spike–LFP coordination in rat prefrontal cortex during working memory. Behav Brain Res 261:297–304. https://doi.org/10.1016/j.bbr.2013.12.030 CrossRefPubMed

Liebe S, Hoerzer GM, Logothetis NK et al (2012) Theta coupling between V4 and prefrontal cortex predicts visual short-term memory performance. Nat Neurosci 15(3):456–462. https://doi.org/10.1038/nn.3038 CrossRefPubMed

Lisman JE, Jensen O (2013) The theta-gamma neural code. Neuron 77(6):1002–1016. https://doi.org/10.1016/j.neuron.2013.03.007 CrossRefPubMedPubMedCentral

Liu J, Xia M, Dai Z et al (2016) Intrinsic brain hub connectivity underlies individual differences in spatial working memory. Cereb Cortex 27(12):1. https://doi.org/10.1093/cercor/bhw317 CrossRef

Lundqvist M, Herman P, Warden MR et al (2018) Gamma and beta bursts during working memory readout suggest roles in its volitional control. Nat Commun 9(1):394. https://doi.org/10.1038/s41467-017-02791-8 CrossRefPubMedPubMedCentral

Nir Y, Andrillon T, Marmelshtein A et al (2017) Selective neuronal lapses precede human cognitive lapses following sleep deprivation. Nat Med 23(12):1474–1480. https://doi.org/10.1038/nm.4433 CrossRefPubMedPubMedCentral

Ouyang M, Li S, Tian X et al (2014) Functional connectivity among spikes in low dimensional space during working memory task in rat. PLoS ONE 9(3):e91481. https://doi.org/10.1371/journal.pone.0091481 CrossRefPubMedPubMedCentral

Riga D, Matos MR, Glas A et al (2016) Optogenetic dissection of medial prefrontal cortex circuitry. Front Syst Neurosci 8:230. https://doi.org/10.3389/fnsys.2014.00230 CrossRef

Romo R, Brody CD, Hernández A et al (1999) Neuronal correlates of parametric working memory in the prefrontal cortex. Nature 399(6735):470–473. https://doi.org/10.1038/20939 CrossRefPubMed

Seymour RA, Rippon G, Kessler K (2017) The detection of phase amplitude coupling during sensory processing. Front Neurosci 11:487. https://doi.org/10.3389/fnins.2017.00487 CrossRefPubMedPubMedCentral

Sorokin JM, Davidson TJ, Frechette E et al (2017) Bidirectional control of generalized epilepsy networks via rapid real-time switching of firing mode. Neuron 93(1):194–210. https://doi.org/10.1016/j.neuron.2016.11.026 CrossRefPubMed

Stevenson RF, Zheng J, Mnatsakanyan L et al (2018) Hippocampal CA1 gamma power predicts the precision of spatial memory judgments. Proc Natl Acad Sci 115(40):10148–10153. https://doi.org/10.1073/pnas.1805724115 CrossRefPubMed

Supriya S, Siuly S, Wang H et al (2016) Weighted visibility graph with complex network features in the detection of epilepsy. IEEE Access 4:6554–6566. https://doi.org/10.1109/access.2016.2612242 CrossRef

Tamburello P, Mili L (2015) New robust estimators of correlation and weighted basis pursuit. IEEE Trans Signal Process 63(4):882–894. https://doi.org/10.1109/tsp.2014.2385664 CrossRef

Toppi J, Astolfi L, Risetti M et al (2018) Different topological properties of EEG-derived networks describe working memory phases as revealed by graph theoretical analysis. Front Hum Neurosci 11:637. https://doi.org/10.3389/fnhum.2017.00637 CrossRefPubMedPubMedCentral

Vatansever D, Manktelow AE, Sahakian BJ et al (2017) Angular default mode network connectivity across working memory load. Hum Brain Mapp 38(1):41–52. https://doi.org/10.1002/hbm.23341 CrossRefPubMed

Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’networks. Nature 393(6684):440. https://doi.org/10.1038/30918 CrossRefPubMed

Wheelock MD, Rangaprakash D, Harnett NG et al (2018) Psychosocial stress reactivity is associated with decreased whole-brain network efficiency and increased amygdala centrality. Behav Neurosci 132(6):561. https://doi.org/10.1037/bne0000276 CrossRefPubMedPubMedCentral

Wutz A, Loonis R, Roy JE et al (2018) Different levels of category abstraction by different dynamics in different prefrontal areas. Neuron 97(3):716–726. https://doi.org/10.1016/j.neuron.2018.01.009 CrossRefPubMedPubMedCentral

Xie J, Bai W, Liu T et al (2014) Functional connectivity among spike trains in neural assemblies during rat working memory task. Behav Brain Res 274:248–257. https://doi.org/10.1016/j.bbr.2014.08.027 CrossRefPubMed

Yeh CH, Lo MT, Hu K (2016) Spurious cross-frequency amplitude–amplitude coupling in nonstationary, nonlinear signals. Phys A 454:143–150. https://doi.org/10.1016/j.physa.2016.02.012 CrossRef

Zhang Y, Li M, Wang R et al (2018) Abnormal brain white matter network in young smokers: a graph theory analysis study. Brain Imaging Behav 12(2):345–356. https://doi.org/10.1007/s11682-017-9699-6 CrossRefPubMed

Zheng W, Yu H, Ding W et al (2018) Changes in brain functional networks of insomniacs induced by magnetic stimulation at acupoints. IEEE Trans Appl Supercond 29(2):1–4. https://doi.org/10.1109/TASC.2018.2882069 CrossRef

Zutshi I, Brandon MP, Fu ML et al (2018) Hippocampal neural circuits respond to optogenetic pacing of theta frequencies by generating accelerated oscillation frequencies. Curr Biol 28(8):1–10. https://doi.org/10.1016/j.cub.2018.02.061 CrossRef

Title: The dynamic properties of a brain network during working memory based on the algorithm of cross-frequency coupling
Authors: Wei Zhang
Lei Guo
Dongzhao Liu
Guizhi Xu
Publication date: 21-11-2019
Publisher: Springer Netherlands
Published in: Cognitive Neurodynamics / Issue 2/2020
Print ISSN: 1871-4080
Electronic ISSN: 1871-4099
DOI: https://doi.org/10.1007/s11571-019-09562-9

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