EEG-Meta-Microstates: Towards a More Objective Use of Resting-State EEG Microstate Findings Across Studies.

EEG Functional brain states Mental states Meta-analysis Microstates Resting-state Spatial similarity

Journal

Brain topography
ISSN: 1573-6792
Titre abrégé: Brain Topogr
Pays: United States
ID NLM: 8903034

Informations de publication

Date de publication:
29 Jul 2023
Historique:
received: 14 06 2023
accepted: 16 07 2023
medline: 29 7 2023
pubmed: 29 7 2023
entrez: 29 7 2023
Statut: aheadofprint

Résumé

Over the last decade, EEG resting-state microstate analysis has evolved from a niche existence to a widely used and well-accepted methodology. The rapidly increasing body of empirical findings started to yield overarching patterns of associations of biological and psychological states and traits with specific microstate classes. However, currently, this cross-referencing among apparently similar microstate classes of different studies is typically done by "eyeballing" of printed template maps by the individual authors, lacking a systematic procedure. To improve the reliability and validity of future findings, we present a tool to systematically collect the actual data of template maps from as many published studies as possible and present them in their entirety as a matrix of spatial similarity. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps from ongoing or published studies. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps in the literature. The analysis of 40 included sets of template maps indicated that: (i) there is a high degree of similarity of template maps across studies, (ii) similar template maps were associated with converging empirical findings, and (iii) representative meta-microstates can be extracted from the individual studies. We hope that this tool will be useful in coming to a more comprehensive, objective, and overarching representation of microstate findings.

Identifiants

pubmed: 37515678
doi: 10.1007/s10548-023-00993-6
pii: 10.1007/s10548-023-00993-6
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2023. The Author(s).

Références

Antonova E, Holding M, Suen HC, Sumich A, Maex R, Nehaniv C (2022) EEG microstates: functional significance and short-term test-retest reliability. Neuroimage Rep 2:100089. https://doi.org/10.1016/j.ynirp.2022.100089
doi: 10.1016/j.ynirp.2022.100089
Artoni F, Maillard J, Britz J, Seeber M, Lysakowski C, Bréchet L, Tramèr MR, Michel CM (2022) EEG microstate dynamics indicate a U-shaped path to propofol-induced loss of consciousness. NeuroImage 256:119156. https://doi.org/10.1016/j.neuroimage.2022.119156
doi: 10.1016/j.neuroimage.2022.119156 pubmed: 35364276
Biswal B, Zerrin Yetkin F, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magn Reson Med 34:537–541. https://doi.org/10.1002/mrm.1910340409
doi: 10.1002/mrm.1910340409 pubmed: 8524021
Bréchet L, Brunet D, Perogamvros L, Tononi G, Michel CM (2020a) EEG microstates of dreams. Sci Rep 10:17069. https://doi.org/10.1038/s41598-020-74075-z
doi: 10.1038/s41598-020-74075-z pubmed: 33051536 pmcid: 7553905
Bréchet L, Brunet D, Perogamvros L, Tononi G, Michel CM (2020b) EEG microstates of dreams. Sci Rep 10:17069. https://doi.org/10.1038/s41598-020-74075-z
doi: 10.1038/s41598-020-74075-z pubmed: 33051536 pmcid: 7553905
Britz J, Van De Ville D, Michel CM (2010) BOLD correlates of EEG topography reveal rapid resting-state network dynamics. NeuroImage 52:1162–1170. https://doi.org/10.1016/j.neuroimage.2010.02.052
doi: 10.1016/j.neuroimage.2010.02.052 pubmed: 20188188
Castellanos FX, Di Martino A, Craddock RC, Mehta AD, Milham MP (2013) Clinical applications of the functional connectome. NeuroImage 80:527–540. https://doi.org/10.1016/j.neuroimage.2013.04.083
doi: 10.1016/j.neuroimage.2013.04.083 pubmed: 23631991
Croce P, Zappasodi F, Capotosto P (2018) Offline stimulation of human parietal cortex differently affects resting EEG microstates. Sci Rep 8:1287. https://doi.org/10.1038/s41598-018-19698-z
doi: 10.1038/s41598-018-19698-z pubmed: 29352255 pmcid: 5775423
Croce P, Quercia A, Costa S, Zappasodi F (2020) EEG microstates associated with intra- and inter-subject alpha variability. Sci Rep 10:2469. https://doi.org/10.1038/s41598-020-58787-w
doi: 10.1038/s41598-020-58787-w pubmed: 32051420 pmcid: 7015936
Croce P, Tecchio F, Tamburro G, Fiedler P, Comani S, Zappasodi F (2022) Brain electrical microstate features as biomarkers of a stable motor output. J Neural Eng 19:056042. https://doi.org/10.1088/1741-2552/ac975b
doi: 10.1088/1741-2552/ac975b
Custo A, Van De Ville D, Wells WM, Tomescu MI, Brunet D, Michel CM (2017) Electroencephalographic resting-state networks: source localization of Microstates. Brain Connect 7:671–682. https://doi.org/10.1089/brain.2016.0476
doi: 10.1089/brain.2016.0476 pubmed: 28938855 pmcid: 5736178
Damborská A, Piguet C, Aubry J-M, Dayer AG, Michel CM, Berchio C (2019a) Altered electroencephalographic resting-state large-scale Brain Network Dynamics in Euthymic Bipolar Disorder Patients. Front Psychiatry 10:826. https://doi.org/10.3389/fpsyt.2019.00826
doi: 10.3389/fpsyt.2019.00826 pubmed: 31803082 pmcid: 6873781
Damborská A, Tomescu MI, Honzírková E, Barteček R, Hořínková J, Fedorová S, Ondruš Å, Michel CM (2019b) EEG Resting-State Large-Scale Brain Network Dynamics Are Related to Depressive Symptoms. Front. Psychiatry 10
Davis SW, Stanley ML, Moscovitch M, Cabeza R (2017) Resting-state networks do not determine cognitive function networks: a commentary on Campbell and Schacter (2016). Lang Cogn Neurosci 32:669–673. https://doi.org/10.1080/23273798.2016.1252847
doi: 10.1080/23273798.2016.1252847 pubmed: 28989941
De Bock R, Mackintosh AJ, Maier F, Borgwardt S, Riecher-Rössler A, Andreou C (2020) EEG microstates as biomarker for psychosis in ultra-high-risk patients. Transl Psychiatry 10:300. https://doi.org/10.1038/s41398-020-00963-7
doi: 10.1038/s41398-020-00963-7 pubmed: 32839449 pmcid: 7445239
Deolindo CS, Ribeiro MW, Aratanha MAA, Scarpari JRS, Forster CHQ, Silva RGA, Machado BS, Amaro Junior E, König T, Kozasa EH (2021) Microstates in complex and dynamical environments: unraveling situational awareness in critical helicopter landing maneuvers. Hum Brain Mapp 42:3168–3181. https://doi.org/10.1002/hbm.25426
doi: 10.1002/hbm.25426 pubmed: 33942444 pmcid: 8193508
Diezig S, Denzer S, Achermann P, Mast FW, Koenig T (2022) EEG Microstate Dynamics Associated with Dream-Like Experiences during the transition to Sleep. Brain Topogr. https://doi.org/10.1007/s10548-022-00923-y
doi: 10.1007/s10548-022-00923-y pubmed: 36402917
Hanoglu L, Toplutas E, Saricaoglu M, Velioglu HA, Yildiz S, Yulug B (2022) Therapeutic role of repetitive transcranial magnetic stimulation in Alzheimer’s and Parkinson’s Disease: Electroencephalography Microstate correlates. Front Neurosci 16
Hu W, Zhang Z, Zhao H, Zhang L, Li L, Huang G, Liang Z (2023) EEG microstate correlates of emotion dynamics and stimulation content during video watching. Cereb. Cortex N. Y. N 1991 33, 523–542. https://doi.org/10.1093/cercor/bhac082
Khanna A, Pascual-Leone A, Farzan F (2014) Reliability of resting-state microstate features in Electroencephalography. PLoS ONE 9:e114163. https://doi.org/10.1371/journal.pone.0114163
doi: 10.1371/journal.pone.0114163 pubmed: 25479614 pmcid: 4257589
Kleinert T, Nash K, Leota J, Koenig T, Heinrichs M, Schiller B (2022) A self-controlled mind is reflected by stable Mental Processing. Psychol Sci 33:2123–2137. https://doi.org/10.1177/09567976221110136
doi: 10.1177/09567976221110136 pubmed: 36279561
Kleinert T, Kalburgi N, Aryan S, Nash D, Schiller K, Koenig B T., n.d. A student’s guide for resting-state microstate analysis
Koenig T, Prichep L, Lehmann D, Sosa PV, Braeker E, Kleinlogel H, Isenhart R, John ER (2002) Millisecond by Millisecond, Year by Year: Normative EEG Microstates and Developmental Stages. NeuroImage 16, 41–48. https://doi.org/10.1006/nimg.2002.1070
Koenig T, Studer D, Hubl D, Melie L, Strik WK (2005) Brain connectivity at different time-scales measured with EEG. Philos Trans R Soc B Biol Sci 360:1015–1024. https://doi.org/10.1098/rstb.2005.1649
doi: 10.1098/rstb.2005.1649
Lakatos I (1978) The methodology of Scientific Research Programmes: philosophical Papers. Cambridge University Press. https://doi.org/10.1017/CBO9780511621123
Lehmann D (1990) Brain Electric Microstates and Cognition: the atoms of Thought. In: John ER, Harmony T, Prichep LS, Valdés-Sosa M, Valdés-Sosa PA (eds) Machinery of the mind: data, theory, and Speculations about higher brain function. Birkhäuser Boston, Boston, MA, pp 209–224. https://doi.org/10.1007/978-1-4757-1083-0_10
doi: 10.1007/978-1-4757-1083-0_10
Lehmann D, Skrandies W (1980) Reference-free identification of components of checkerboard-evoked multichannel potential fields. Electroencephalogr Clin Neurophysiol 48:609–621. https://doi.org/10.1016/0013-4694(80)90419-8
doi: 10.1016/0013-4694(80)90419-8 pubmed: 6155251
Lehmann D, Ozaki H, Pal I (1987) EEG alpha map series: brain micro-states by space-oriented adaptive segmentation. Electroencephalogr Clin Neurophysiol 67:271–288. https://doi.org/10.1016/0013-4694(87)90025-3
doi: 10.1016/0013-4694(87)90025-3 pubmed: 2441961
Lehmann D, Strik WK, Henggeler B, Koenig T, Koukkou M (1998) Brain electric microstates and momentary conscious mind states as building blocks of spontaneous thinking: I. Visual imagery and abstract thoughts. Int J Psychophysiol 29:1–11. https://doi.org/10.1016/S0167-8760(97)00098-6
doi: 10.1016/S0167-8760(97)00098-6 pubmed: 9641243
Lehmann D, Pascual-Marqui RD, Strik WK, Koenig T (2010) Core networks for visual-concrete and abstract thought content: a brain electric microstate analysis. NeuroImage 49:1073–1079. https://doi.org/10.1016/j.neuroimage.2009.07.054
doi: 10.1016/j.neuroimage.2009.07.054 pubmed: 19646538
Liu J, Xu J, Zou G, He Y, Zou Q, Gao J-H (2020) Reliability and individual specificity of EEG microstate characteristics. Brain Topogr 33:438–449. https://doi.org/10.1007/s10548-020-00777-2
doi: 10.1007/s10548-020-00777-2 pubmed: 32468297
Michel CM, Koenig T (2018) EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: a review. NeuroImage. Brain Connectivity Dynamics 180:577–593. https://doi.org/10.1016/j.neuroimage.2017.11.062
doi: 10.1016/j.neuroimage.2017.11.062
Michel CM, Murray MM (2012) Towards the utilization of EEG as a brain imaging tool. NeuroImage 61:371–385. https://doi.org/10.1016/j.neuroimage.2011.12.039
doi: 10.1016/j.neuroimage.2011.12.039 pubmed: 22227136
Milz P, Faber PL, Lehmann D, Koenig T, Kochi K, Pascual-Marqui RD (2016) The functional significance of EEG microstates—Associations with modalities of thinking. NeuroImage 125:643–656. https://doi.org/10.1016/j.neuroimage.2015.08.023
doi: 10.1016/j.neuroimage.2015.08.023 pubmed: 26285079
Murphy M, Stickgold R, Parr ME, Callahan C, Wamsley EJ (2018) Recurrence of task-related electroencephalographic activity during post-training quiet rest and sleep. Sci Rep 8:5398. https://doi.org/10.1038/s41598-018-23590-1
doi: 10.1038/s41598-018-23590-1 pubmed: 29599462 pmcid: 5876367
Musaeus CS, Engedal K, Høgh P, Jelic V, Khanna AR, Kjær TW, Mørup M, Naik M, Oeksengaard A, Santarnecchi E, Snaedal J, Wahlund L, Waldemar G, Andersen BB (2020) Changes in the left temporal microstate are a sign of cognitive decline in patients with Alzheimer’s disease. Brain Behav 10. https://doi.org/10.1002/brb3.1630
Nagabhushan Kalburgi S, Whitten AP, Key AP, Bodfish JW (2020) Children with autism produce a Unique Pattern of EEG Microstates during an eyes closed resting-state Condition. Front Hum Neurosci 14:288. https://doi.org/10.3389/fnhum.2020.00288
doi: 10.3389/fnhum.2020.00288 pubmed: 33132865 pmcid: 7579608
Nash K, Kleinert T, Leota J, Scott A, Schimel J (2022) Resting-state networks of believers and non-believers: an EEG microstate study. Biol Psychol 169:108283. https://doi.org/10.1016/j.biopsycho.2022.108283
doi: 10.1016/j.biopsycho.2022.108283 pubmed: 35114302
Notturno F, Croce P, Ornello R, Sacco S, Zappasodi F (2023) Yield of EEG features as markers of disease severity in amyotrophic lateral sclerosis: a pilot study. Amyotroph Lateral Scler Front Degener 24:295–303. https://doi.org/10.1080/21678421.2022.2152696
doi: 10.1080/21678421.2022.2152696
Pascual-Marqui RD, Michel CM, Lehmann D (1995) Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE Trans Biomed Eng 42:658–665. https://doi.org/10.1109/10.391164
doi: 10.1109/10.391164 pubmed: 7622149
Perrin F, Pernier J, Bertrand O, Echallier JF (1989) Spherical splines for scalp potential and current density mapping. Electroencephalogr Clin Neurophysiol 72:184–187. https://doi.org/10.1016/0013-4694(89)90180-6
doi: 10.1016/0013-4694(89)90180-6 pubmed: 2464490
Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL (2001) A default mode of brain function. Proc. Natl. Acad. Sci. 98, 676–682. https://doi.org/10.1073/pnas.98.2.676
Ricci L, Croce P, Lanzone J, Boscarino M, Zappasodi F, Tombini M, Di Lazzaro V, Assenza G (2020) Transcutaneous Vagus nerve stimulation modulates EEG microstates and Delta Activity in healthy subjects. Brain Sci 10:668. https://doi.org/10.3390/brainsci10100668
doi: 10.3390/brainsci10100668 pubmed: 32992726 pmcid: 7599782
Ricci L, Croce P, Pulitano P, Boscarino M, Zappasodi F, Narducci F, Lanzone J, Sancetta B, Mecarelli O, Di Lazzaro V, Tombini M, Assenza G (2022) Levetiracetam modulates EEG microstates in temporal lobe Epilepsy. Brain Topogr 35:680–691. https://doi.org/10.1007/s10548-022-00911-2
doi: 10.1007/s10548-022-00911-2 pubmed: 36098891 pmcid: 9684258
Rieger K, Diaz Hernandez L, Baenninger A, Koenig T (2016) 15 years of Microstate Research in Schizophrenia – where are we? A Meta-analysis. Front Psychiatry 7. https://doi.org/10.3389/fpsyt.2016.00022
Schiller B, Koenig T, Heinrichs M (2019) Oxytocin modulates the temporal dynamics of resting EEG networks. Sci Rep 9:13418. https://doi.org/10.1038/s41598-019-49636-6
doi: 10.1038/s41598-019-49636-6 pubmed: 31558733 pmcid: 6763457
Schiller B, Kleinert T, Teige-Mocigemba S, Klauer KC, Heinrichs M (2020) Temporal dynamics of resting EEG networks are associated with prosociality. Sci Rep 10:13066. https://doi.org/10.1038/s41598-020-69999-5
doi: 10.1038/s41598-020-69999-5 pubmed: 32747655 pmcid: 7400630
Smailovic U, Koenig T, Laukka EJ, Kalpouzos G, Andersson T, Winblad B, Jelic V (2019) EEG time signature in Alzheimer´s disease: functional brain networks falling apart. NeuroImage Clin 24:102046. https://doi.org/10.1016/j.nicl.2019.102046
doi: 10.1016/j.nicl.2019.102046 pubmed: 31795039 pmcid: 6909352
Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM, Mackay CE, Filippini N, Watkins KE, Toro R, Laird AR, Beckmann CF (2009) Correspondence of the brain’s functional architecture during activation and rest. Proc. Natl. Acad. Sci. 106, 13040–13045. https://doi.org/10.1073/pnas.0905267106
Spring JN, Tomescu MI, Barral J (2017) A single-bout of endurance Exercise modulates EEG microstates temporal features. Brain Topogr 30:461–472. https://doi.org/10.1007/s10548-017-0570-2
doi: 10.1007/s10548-017-0570-2 pubmed: 28528447
Spring JN, Bourdillon N, Barral J (2018) Resting EEG microstates and autonomic heart rate variability do not return to Baseline one Hour after a Submaximal Exercise. Front Neurosci 12
Spring JN, Sallard EF, Trabucchi P, Millet GP, Barral J (2022) Alterations in spontaneous electrical brain activity after an extreme mountain ultramarathon. Biol Psychol 171:108348. https://doi.org/10.1016/j.biopsycho.2022.108348
doi: 10.1016/j.biopsycho.2022.108348 pubmed: 35569573
Tarailis P, Šimkutė D, Koenig T, Griškova-Bulanova I (2021) Relationship between Spatiotemporal Dynamics of the brain at Rest and Self-Reported spontaneous thoughts: an EEG Microstate Approach. J Pers Med 11:1216. https://doi.org/10.3390/jpm11111216
doi: 10.3390/jpm11111216 pubmed: 34834568 pmcid: 8625384
Tarailis P, Koenig T, Michel CM, Griškova-Bulanova I (2023) The functional aspects of resting EEG microstates: a systematic review. Brain Topogr. https://doi.org/10.1007/s10548-023-00958-9
doi: 10.1007/s10548-023-00958-9 pubmed: 37162601
Tomescu MI, Rihs TA, Rochas V, Hardmeier M, Britz J, Allali G, Fuhr P, Eliez S, Michel CM (2018) From swing to cane: sex differences of EEG resting-state temporal patterns during maturation and aging. Dev Cogn Neurosci 31:58–66. https://doi.org/10.1016/j.dcn.2018.04.011
doi: 10.1016/j.dcn.2018.04.011 pubmed: 29742488 pmcid: 6969216
Tomescu MI, Papasteri CC, Sofonea A, Boldasu R, Kebets V, Pistol CAD, Poalelungi C, Benescu V, Podina IR, Nedelcea CI, Berceanu AI, Carcea I (2022) Spontaneous thought and microstate activity modulation by social imitation. NeuroImage 249:118878. https://doi.org/10.1016/j.neuroimage.2022.118878
doi: 10.1016/j.neuroimage.2022.118878 pubmed: 34999201
Vellante F, Ferri F, Baroni G, Croce P, Migliorati D, Pettoruso M, De Berardis D, Martinotti G, Zappasodi F, Giannantonio MD (2020) Euthymic bipolar disorder patients and EEG microstates: a neural signature of their abnormal self experience? J Affect Disord 272:326–334. https://doi.org/10.1016/j.jad.2020.03.175
doi: 10.1016/j.jad.2020.03.175 pubmed: 32553374
Wackermann J, Lehmann D, Michel CM, Strik WK (1993) Adaptive segmentation of spontaneous EEG map series into spatially defined microstates. Int J Psychophysiol 14:269–283. https://doi.org/10.1016/0167-8760(93)90041-M
doi: 10.1016/0167-8760(93)90041-M pubmed: 8340245
Zanesco AP, King BG, Skwara AC, Saron CD (2020) Within and between-person correlates of the temporal dynamics of resting EEG microstates. NeuroImage 211:116631. https://doi.org/10.1016/j.neuroimage.2020.116631
doi: 10.1016/j.neuroimage.2020.116631 pubmed: 32062082
Zanesco AP, Denkova E, Jha AP (2021a) Self-reported mind Wandering and Response Time Variability Differentiate Prestimulus Electroencephalogram Microstate Dynamics during a sustained attention Task. J Cogn Neurosci 33:28–45. https://doi.org/10.1162/jocn_a_01636
doi: 10.1162/jocn_a_01636 pubmed: 33054554
Zanesco AP, Denkova E, Jha AP (2021b) Associations between self-reported spontaneous thought and temporal sequences of EEG microstates. Brain Cogn 150:105696. https://doi.org/10.1016/j.bandc.2021.105696
doi: 10.1016/j.bandc.2021.105696 pubmed: 33706148
Zanesco AP, Skwara AC, King BG, Powers C, Wineberg K, Saron CD (2021c) Meditation training modulates brain electric microstates and felt states of awareness. Hum Brain Mapp 42:3228–3252. https://doi.org/10.1002/hbm.25430
doi: 10.1002/hbm.25430 pubmed: 33783922 pmcid: 8193519
Zappasodi F, Croce P, Giordani A, Assenza G, Giannantoni NM, Profice P, Granata G, Rossini PM, Tecchio F (2017) Prognostic Value of EEG Microstates in Acute Stroke. Brain Topogr 30:698–710. https://doi.org/10.1007/s10548-017-0572-0
doi: 10.1007/s10548-017-0572-0 pubmed: 28547185
Zappasodi F, Perrucci MG, Saggino A, Croce P, Mercuri P, Romanelli R, Colom R, Ebisch SJH (2019) EEG microstates distinguish between cognitive components of fluid reasoning. NeuroImage 189:560–573. https://doi.org/10.1016/j.neuroimage.2019.01.067
doi: 10.1016/j.neuroimage.2019.01.067 pubmed: 30710677

Auteurs

Thomas Koenig (T)

Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland. thomas.koenig@unibe.ch.
Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, Huddinge, Sweden. thomas.koenig@unibe.ch.
Children's Hospital Los Angeles, The Saban Research Institute, Los Angeles, CA, 90027, USA. thomas.koenig@unibe.ch.

Sarah Diezig (S)

Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland.

Sahana Nagabhushan Kalburgi (SN)

Children's Hospital Los Angeles, The Saban Research Institute, Los Angeles, CA, 90027, USA.

Elena Antonova (E)

Division of Psychology, Department of Life Sciences, College of Health, Medicine and Life Sciences & Centre for Cognitive Neuroscience, Brunel University London, Kingston Lane, Uxbridge, UB8 3PH, UK.

Fiorenzo Artoni (F)

Human Neuron Lab, Faculty of Medicine, Department of Clinical Neurosciences, University of Geneva, Geneva, Switzerland.

Lucie Brechet (L)

Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, Geneva, 1202, Switzerland.

Juliane Britz (J)

Department of Psychology, University of Fribourg, Fribourg, Switzerland.

Pierpaolo Croce (P)

Department of Neurosciences, Imaging and Clinical Sciences, Behavioral Imaging and Neural Dynamics Center, Institute for Advanced Biomedical Technologies, "Gabriele d'Annunzio" University, Chieti, 66100, Italy.

Anna Custo (A)

Department of Nuclear Medicine, Geneva University Hospital (HUG), Geneva, Switzerland.

Alena Damborská (A)

Department of Psychiatry, Faculty of Medicine, University Hospital Brno, Masaryk University, Brno, Czechia.

Camila Deolindo (C)

Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark.

Markus Heinrichs (M)

Department of Psychology, Laboratory for Biological Psychology, Clinical Psychology and Psychotherapy, Albert-Ludwigs-University of Freiburg, Breisgau, Germany.

Tobias Kleinert (T)

Department of Psychology, Laboratory for Biological Psychology, Clinical Psychology and Psychotherapy, Albert-Ludwigs-University of Freiburg, Breisgau, Germany.
Department of Ergonomics, Leibniz Research Centre for Working Environment and Human Factors, Ardeystr. 67, Dortmund, 44139, Germany.

Zhen Liang (Z)

School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, China.

Michael M Murphy (MM)

Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
McLean Hospital, Belmont, MA, USA.

Kyle Nash (K)

Department of Psychology, University of Alberta, Edmonton, AB, T6G 2E9, Canada.

Chrystopher Nehaniv (C)

Departments of Systems Design Engineering and Electrical & Computer Engineering, University of Waterloo, 200 University Avenue W, Waterloo, ON, N2L 3G1, Canada.

Bastian Schiller (B)

Department of Psychology, Laboratory for Biological Psychology, Clinical Psychology and Psychotherapy, Albert-Ludwigs-University of Freiburg, Breisgau, Germany.

Una Smailovic (U)

Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, Huddinge, Sweden.
Department of Clinical Neurophysiology, Karolinska University Hospital, Stockholm, Sweden.

Povilas Tarailis (P)

Life Sciences Centre, Institute of Biosciences, Vilnius University, Vilnius, Lithuania.

Miralena Tomescu (M)

CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania.
Faculty of Educational Sciences, Department of Psychology, University "Stefan cel Mare" of Suceava, Suceava, Romania.
Faculty of Psychology and Educational Sciences, Department of Cognitive Sciences, University of Bucharest, Bucharest, Romania.

Eren Toplutaş (E)

Department of Neurology, Istanbul Eyupsultan Public Hospital, Istanbul, Turkey.
Program of Neuroscience Ph.D, Graduate School of Health Sciences, Istanbul Medipol University, Istanbul, Turkey.

Federica Vellante (F)

Department of Neurosciences, Imaging and Clinical Sciences, Behavioral Imaging and Neural Dynamics Center, Institute for Advanced Biomedical Technologies, "Gabriele d'Annunzio" University, Chieti, 66100, Italy.

Anthony Zanesco (A)

Department of Psychology, University of Miami, Coral Gables, FL, USA.

Filippo Zappasodi (F)

Department of Neurosciences, Imaging and Clinical Sciences, Behavioral Imaging and Neural Dynamics Center, Institute for Advanced Biomedical Technologies, "Gabriele d'Annunzio" University, Chieti, 66100, Italy.

Qihong Zou (Q)

Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China.

Christoph M Michel (CM)

Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, Geneva, 1202, Switzerland.

Classifications MeSH