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Cognitive Neurodynamics

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25.07.2022 | Research Article

Investigation of global brain dynamics depending on emotion regulation strategies indicated by graph theoretical brain network measures at system level

verfasst von: Serap Aydın

Erschienen in: Cognitive Neurodynamics | Ausgabe 2/2023

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Abstract

In the present study, new findings reveal the close association between graph theoretic global brain connectivity measures and cognitive abilities the ability to manage and regulate negative emotions in healthy adults. Functional brain connectivity measures have been estimated from both eyes-opened and eyes-closed resting-state EEG recordings in four groups including individuals who use opposite Emotion Regulation Strategies (ERS) as follow: While 20 individuals who frequently use two opposing strategies, such as rumination and cognitive distraction, are included in 1st group, 20 individuals who don’t use these cognitive strategies are included in 2nd group. In 3rd and 4th groups, there are matched individuals who use both Expressive Suppression and Cognitive Reappraisal strategies together frequently and never use them, respectively. EEG measurements and psychometric scores of individuals were both downloaded from a public dataset LEMON. Since it is not sensitive to volume conduction, Directed Transfer Function has been applied to 62-channel recordings to obtain cortical connectivity estimations across the whole cortex. Regarding well defined threshold, connectivity estimations have been transformed into binary numbers for implementation of Brain Connectivity Toolbox. The groups are compared to each other through both statistical logistic regression models and deep learning models driven by frequency band specific network measures referring segregation, integration and modularity of the brain. Overall results show that high classification accuracies of 96.05% (1st vs 2nd) and 89.66% (3rd vs 4th) are obtained in analyzing full-band (\(0.5-45~Hz\)) EEG. In conclusion, negative strategies may upset the balance between segregation and integration. In particular, graphical results show that frequent use of rumination induces the decrease in assortativity referring network resilience. The psychometric scores are found to be highly correlated with brain network measures of global efficiency, local efficiency, clustering coefficient, transitivity and assortativity in even resting-state.

Vorheriger Artikel Cross-subject aesthetic preference recognition of Chinese dance posture using EEG

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Anastasiadou MN et al (2019) Graph theoretical characteristics of EEG-based functional brain networks in patients with epilepsy: the effect of reference choice and volume conduction. Front Neurosci. https://doi.org/10.3389/fnins.2019.00221CrossRefPubMedPubMedCentral

Aydın S (2021) Cross-validated adaboost classification of emotion regulation strategies identified by spectral coherence in resting-state. Neuroinformatics. https://doi.org/10.1007/s12021-021-09542-7CrossRefPubMed

Babayan A et al (2019) Data Descriptor: A mind-brainbody dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults. Data, Scien. https://doi.org/10.1038/sdata.2018.308CrossRef

Batbaatar E, Li M, Ryu KH (2019) Semantic-emotion neural network for emotion recognition from text. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2934529CrossRef

Bianchi AM et al (2013) Frequency-based approach to the study of semantic brain networks connectivity. J Neurosci Meth 212(2):181–189CrossRef

Blinowska KJ, Kaminski M (2006) Multivariate Signal Analysis by Parametric Models, Handbook of Time Series Analysis: 387-420

Blinowska KJ et al (2004) Granger causality and information flow in multivariate processes. Phys Rev EE 70:050902CrossRef

Blinowska KJ et al (2010) Transmission of brain activity during cognitive task. Brain Topogr. https://doi.org/10.1007/s10548-010-0137-yCrossRefPubMedPubMedCentral

Blinowska KJ, Rakowski F et al (2017) Functional and effective brain connectivity for discrimination between Alzheimer’s patients and healthy individuals: A study on resting state EEG rhythms. Clin Neurophysiol. https://doi.org/10.1016/j.clinph.2016.10.002CrossRefPubMed

Broyd SJ et al (2009) Default-mode brain dysfunction in mental disorders: A systematic review. Neurosci Biobehav Rev 33(3):279–296CrossRefPubMed

Brzezicka A et al (2010) Information transfer during transitive reasoning task. Brain Topogr. https://doi.org/10.1007/s10548-010-0158-6CrossRefPubMedPubMedCentral

Buhle JT et al (2014) Cognitive reappraisal of emotion: a meta-analysis of human neuroimaging studies. Cereb Cortex. https://doi.org/10.1093/cercor/bht154CrossRefPubMed

Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. https://doi.org/10.1038/nrn2575CrossRefPubMed

Bullmore E, Sporns O (2012) The economy of brain network organization. Nat Rev Neurosci 13:336–349. https://doi.org/10.1038/nrn3214CrossRefPubMed

Cabeza R et al (2018) Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing. Nat Rev Neurosci 19:701–710CrossRefPubMedPubMedCentral

Cao M et al (2014) Topological organization of the human brain functional connectome across the lifespan. Dev Cogn Neurosci 7:76–93CrossRefPubMed

Chen JX, Zhang PW et al (2019) Accurate EEG-based emotion recognition on combined features using deep convolutional neural networks. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2908285CrossRefPubMedPubMedCentral

Christian G, Gereon R (2011) Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches. Brain. https://doi.org/10.1093/brain/awr033CrossRef

Crespel A, Gelisse P et al (2005) Atlas of Electroencephalography, vol 1, 1st edn. J. Libbey Eurotext, Paris, France

Diekhof EK et al (2011) Fear is only as deep as the mind allows. Neuroim. https://doi.org/10.1016/j.neuroimage.2011.05.073CrossRef

Friston KJ (1994) Functional and effective connectivity in neuroimaging: a synthesis. Hum Brain Mapp 2:56–78. https://doi.org/10.1002/hbm.460020107CrossRef

Gao W, Gilmore JH et al (2011) Temporal and spatial evolution of brain network topology during the first two years of life. PLoS ONE 6:e25278. https://doi.org/10.1371/journal.pone.0025278CrossRefPubMedPubMedCentral

Ghahari S, Farahani N et al (2020) Investigating time-varying functional connectivity derived from the Jackknife Correlation method for distinguishing between emotions in fMRI data. Cogn Neurodyn 14(4):457–471. https://doi.org/10.1007/s11571-020-09579-5CrossRefPubMedPubMedCentral

Giuliani NR et al (2011) Emotion regulation and brain plasticity: expressive suppression use predicts anterior insula volume. Neuroimage. https://doi.org/10.1016/j.neuroimage.2011.06.028CrossRefPubMed

Giuliani NR et al (2011) Anterior cingulate cortex volume and emotion regulation: is bigger better? Biol Psychol. https://doi.org/10.1016/j.biopsycho.2010.11.010CrossRefPubMed

Goldin PR et al (2008) The neural bases of emotion regulation: reappraisal and suppression of negative emotion. Biol Psych 63:577–586CrossRef

Gorantla VR et al (2020) Associations of alpha and beta interhemispheric EEG coherences with indices of attentional control and academic performance. Behav Neurol. https://doi.org/10.1155/2020/4672340CrossRefPubMedPubMedCentral

Gross JJ (2002) Emotion regulation: affective, cognitive, and social consequences. Psychoph 39(3):281–91CrossRef

Gross JJ, Jazaieri H (2014) Emotion, emotion regulation, and psychopathology: An affective science perspective. Psych Science, Clin. https://doi.org/10.1177/2167702614536164CrossRef

Hasanzadeh F et al (2020) Graph theory analysis of directed functional brain networks in major depressive disorder based on EEG signal. J Neural Eng. https://doi.org/10.1088/1741-2552/ab7613CrossRefPubMed

He B et al (2011) eConnectome: A MATLAB toolbox for mapping and imaging of brain functional connectivity. J Neurosci Met. https://doi.org/10.1016/j.jneumeth.2010.11.015CrossRef

Hinton GE et al (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554CrossRefPubMed

Hochreiter S et al (1997) Long short-term memory. Neural Comput 9(8):1735–1780CrossRefPubMed

Jiang P, Fu H et al (2019) Parallelized convolutional recurrent neural network with spectral features for speech emotion recognition. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2927384CrossRefPubMedPubMedCentral

Kalokerinos EK et al (2014) Emotion reappraisal but not suppression down regulates the experience of positive and negative emotion. Emotion. https://doi.org/10.1037/emo0000025CrossRefPubMed

Korzeniewska A et al (2003) Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method. J Neurosci Meth 125(1):195–207CrossRef

Korzeniewska A et al (2014) Ictal propagation of high frequency activity is recapitulated in interictal recordings: Effective connectivity of epileptogenic networks recorded with intracranial EEG. NeuroImage 101:96–113CrossRefPubMed

Kudo M et al (1999) Multidimensional curve classification using passing-through regions. Patte Rec Lett 20(11–13):1103–1111CrossRef

Latora V, Marchiori M (2001) Efficient behavior of small-world networks. Phys Rev Lett 87(19):198701. https://doi.org/10.1103/PhysRevLett.87.198701CrossRefPubMed

Lee TW et al (2008) Controlling emotional expression: behavioral and neural correlates of nonimitative emotional responses. Cereb Cortex. https://doi.org/10.1093/cercor/bhm035CrossRefPubMedPubMedCentral

Li BJ, Friston K et al (2018) A brain network model for depression: from symptom understanding to disease intervention. CNS Neurosci Ther 24(11):1004–1019. https://doi.org/10.1111/cns.12998CrossRefPubMedPubMedCentral

Li THS, Kuo PH et al (2019) CNN and LSTM based facial expression analysis model for a humanoid robot. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2928364CrossRefPubMedPubMedCentral

Ligeza TS, Wyczesany M et al (2016) Interactions between the prefrontal cortex and attentional systems during volitional affective regulation: an effective connectivity reappraisal study. Brain Topogr. https://doi.org/10.1007/s10548-015-0454-2CrossRefPubMed

Ligeza TS, Wyczesany M (2017) Cognitive conflict increases processing of negative, task-irrelevant stimuli. J. of Psychop, Int. https://doi.org/10.1016/j.ijpsycho.2017.07.013CrossRef

Liu X, Wu X et al (2020) Dynamic properties of Human default mode network in Eyes-closed and eyes-open. Brain Topogr 33:720–732. https://doi.org/10.1007/s10548-020-00792-3CrossRefPubMed

Lynall ME et al (2010) Functional connectivity and brain networks in schizophrenia. J of Neurosci. https://doi.org/10.1523/JNEUROSCI.0333-10CrossRef

McDuff D, Jun E et al (2019) Longitudinal observational evidence of the impact of emotion regulation strategies on affective expression. Comp, IEEE Trans on Affe. https://doi.org/10.1109/TAFFC.2019.2961912CrossRef

Neumaier A, Schneider T (2001) Estimation of parameters and eigenmodes of multivariate autoregressive models. ACM Trans on Math Soft. https://doi.org/10.1145/382043.382304CrossRef

Newman MEJ (2002) Assortative mixing in networks. Phys Rev Lett 89(20):208701CrossRefPubMed

Ochsner KN, Gross JJ (2008) Cognitive emotion regulation: insights from social cognitive and affective neuroscience. Curr Direc in Psych Sci 17:153–158CrossRef

Olejarczyk E, Jozwik A et al (2021) Statistical analysis of Graph-Theoretic indices to study EEG-TMS connectivity in patients with depression. Neuroinform, Front. https://doi.org/10.3389/fninf.2021.651082CrossRef

Ouyang G, Zhou C. Characterizing the brain’s dynamical response from scalp-level neural electrical signals: a review of methodology development. Cogn Neurodyn. 14(6): 731-742 https://doi.org/10.1007/s11571-020-09631-4

Pham TTD, Won CS (2019) Facial action units for training convolutional neural networks. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2921241CrossRef

Phan KL et al (2005) Neural substrates for voluntary suppression of negative affect: a functional magnetic resonance imaging study. Biol Psych 57:210–219CrossRef

Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: Uses and interpretations. NeuroImage. https://doi.org/10.1016/j.neuroimage.2009.10.003CrossRefPubMed

Schllögl A (2002) Time Series Analysis, A toolbox for the use with Matlab 1996-2002 http://biosig-consulting.com/matlab/tsa/

Schneider TA, Neumaier A (2001) Algorithm 808: ARfit-A Matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models. ACM Trans Math Softw 27:58–65CrossRef

Shen F, Dai G et al (2020) EEG-based emotion recognition using 4D convolutional recurrent neural network. Cogn Neurodyn 14:815–828CrossRefPubMedPubMedCentral

Stam CJ et al (2009) Graph theoretical analysis of magnetoencephalographic functional connectivity in Alazheimer disease. Brain. https://doi.org/10.1093/brain/awn262CrossRefPubMed

Stam CJ (2014) Modern network science of neurological disorders. Nat Rev Neurosci. https://doi.org/10.1038/nrn3801CrossRefPubMed

Stern Y et al (2018) Whitepaper: defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimers Dement. https://doi.org/10.1016/j.jalz.2018.07.219CrossRefPubMedPubMedCentral

Tijms BM, Wink AM et al (2013) Alzheimer’s disease: connecting findings from graph theoretical studies of brain networks. Neurob of Aging. https://doi.org/10.1016/j.neurobiolaging.2013.02.020CrossRef

van Dellen E et al (2015) Loss of EEG network efficiency is related to cognitive impairment in dementia with lewy bodies. Mov Disord. https://doi.org/10.1002/mds.26309CrossRefPubMed

Vigon L, Saatchi MR et al (2000) (2000) Quantitative evaluation of techniques for ocular artefact filtering of EEG waveforms. IEE Proc Sci Measurement Tech 147(5):219–228CrossRef

Wang K et al (2017) MRI correlates of interaction between gender and expressive suppression among the Chinese population. Neurosci 2017(01):042. https://doi.org/10.1016/j.neuroscienceCrossRef

Wang D et al (2018) Epileptic seizure detection in long-term EEG recordings by using wavelet-based directed transfer function. IEEE Trans on BME. https://doi.org/10.1109/TBME.2018.2809798CrossRef

Wang G et al (2020) Seizure prediction using Directed Transfer Function and convolution neural network on intracranial EEG. IEEE Trans on Neural Sys and Reh Eng. https://doi.org/10.1109/TNSRE.2020.3035836CrossRef

Wang R, Liu MX et al (2021) Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities. Proc of the Nat Acam of Sci, USA,. https://doi.org/10.1073/pnas.2022288118CrossRef

Webb TL et al (2012) Dealing with feeling: a meta-analysis of the effectiveness of strategies derived from the process model of emotion regulation. Psych Bull 138(4):775–808CrossRef

Wilke C et al (2010) Neocortical seizure foci localization by means of a directed transfer function method. Epilepsia. https://doi.org/10.1111/j.1528-1167.2009.02329.xCrossRefPubMedPubMedCentral

Wilke C et al (2011) Graph analysis of epileptogenic networks in human partial epilepsy. Epilepsia. https://doi.org/10.1111/j.1528-1167.2010.02785.xCrossRefPubMed

Wyczesany M, Ligeza TS, Grzybowski SJ (2014) Effective connectivity during visual processing is affected by emotional state. Brain Imag Beh. https://doi.org/10.1007/s11682-014-9326-8CrossRef

Zheng S et al (2018) A deep learning approach to software evolution. Int J Comp App in Tech 58(3):175–183CrossRef

Titel: Investigation of global brain dynamics depending on emotion regulation strategies indicated by graph theoretical brain network measures at system level
verfasst von: Serap Aydın
Publikationsdatum: 25.07.2022
Verlag: Springer Netherlands
Erschienen in: Cognitive Neurodynamics / Ausgabe 2/2023
Print ISSN: 1871-4080
Elektronische ISSN: 1871-4099
DOI: https://doi.org/10.1007/s11571-022-09843-w

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