Region-specific complexity of the intracranial EEG in the sleeping human brain.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
10 01 2022
10 01 2022
Historique:
received:
26
07
2021
accepted:
13
12
2021
entrez:
11
1
2022
pubmed:
12
1
2022
medline:
25
2
2022
Statut:
epublish
Résumé
As the brain is a complex system with occurrence of self-similarity at different levels, a dedicated analysis of the complexity of brain signals is of interest to elucidate the functional role of various brain regions across the various stages of vigilance. We exploited intracranial electroencephalogram data from 38 cortical regions using the Higuchi fractal dimension (HFD) as measure to assess brain complexity, on a dataset of 1772 electrode locations. HFD values depended on sleep stage and topography. HFD increased with higher levels of vigilance, being highest during wakefulness in the frontal lobe. HFD did not change from wake to stage N2 in temporo-occipital regions. The transverse temporal gyrus was the only area in which the HFD did not differ between any two vigilance stages. Interestingly, HFD of wakefulness and stage R were different mainly in the precentral gyrus, possibly reflecting motor inhibition in stage R. The fusiform and parahippocampal gyri were the only areas showing no difference between wakefulness and N2. Stages R and N2 were similar only for the postcentral gyrus. Topographical analysis of brain complexity revealed that sleep stages are clearly differentiated in fronto-central brain regions, but that temporo-occipital regions sleep differently.
Identifiants
pubmed: 35013431
doi: 10.1038/s41598-021-04213-8
pii: 10.1038/s41598-021-04213-8
pmc: PMC8748934
doi:
Types de publication
Journal Article
Multicenter Study
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
451Subventions
Organisme : the Natural Sciences and Engineering Research Council of Canada
ID : RGPIN-2020-04127 and RGPAS-2020-00021
Organisme : the Fonds de Recherche du Québec - Santé
ID : Chercheur-boursier clinicien Senior" award of the Fonds de Recherche du Québec - Santé 2021-2025.
Informations de copyright
© 2022. The Author(s).
Références
Brodmann, K. Vergleichende Lokalisationslehre der Großhirnrinde (Verlag von Johann Ambrosius Barth, 1909).
Evans, A.C., Collins, D.L., Milner, B. An MRI-based stereotaxic atlas from 250 young normal subjects. Proc 22nd Annual Symposium, Society for Neuroscience 18, 408, (1992a).
Evans, A. C. et al. Anatomical mapping of functional activation in stereotactic coordinate space. Neuroimage 1(1), 43–63 (1992).
pubmed: 9343556
doi: 10.1016/1053-8119(92)90006-9
Evans, A.C., Collins, D.L., Mills, S.R., et al. 3D statis- tical neuroanatomical models from 305 MRI volumes. Proc IEEE-Nuclear Science Symposium and Medical Imaging Conference, pp. 1813–1817, (1993c).
Collins, D. L. et al. Automatic 3-D intersubject registration of MR volumetric data in standardized Talairach space. J. Comput. Assist. Tomogr. 18(2), 192–205 (1994).
pubmed: 8126267
doi: 10.1097/00004728-199403000-00005
Mazziotta, J. et al. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos. Trans. R. Soc. Lond. B Biol. Sci. 356, 1293–1322 (2001).
pubmed: 11545704
pmcid: 1088516
doi: 10.1098/rstb.2001.0915
von Ellenrieder, N. et al. Sparse asynchronous cortical generators can produce measurable scalp EEG signals. Neuroimage 138, 123–133 (2016).
doi: 10.1016/j.neuroimage.2016.05.067
Grech, R. et al. Review on solving the inverse problem in EEG source analysis. J. Neuroeng. Rehabil. 5, 25 (2008).
pubmed: 18990257
pmcid: 2605581
doi: 10.1186/1743-0003-5-25
Asadzadeh, S. et al. A systematic review of EEG source localization techniques and their applications on diagnosis of brain abnormalities. J. Neurosci. Methods 339, 108740 (2020).
pubmed: 32353472
doi: 10.1016/j.jneumeth.2020.108740
Frauscher, B. et al. Scalp spindles are associated with widespread intracranial activity with unexpectedly low synchrony. Neuroimage 105, 1–12 (2015).
pubmed: 25450108
doi: 10.1016/j.neuroimage.2014.10.048
Frauscher, B. et al. Atlas of the normal intracranial electroencephalogram: Neurophysiological awake activity in different cortical areas. Brain 141(4), 1130 (2018).
pubmed: 29506200
doi: 10.1093/brain/awy035
Frauscher, B. et al. High-frequency oscillations in the normal human brain. Ann. Neurol. 84(3), 374–385 (2018).
pubmed: 30051505
doi: 10.1002/ana.25304
von Ellenrieder, N. et al. How the human brain sleeps: Direct cortical recordings of normal brain activity. Ann. Neurol. 87(2), 289–301 (2020).
doi: 10.1002/ana.25651
https://mni-open-ieegatlas.research.mcgill.ca
Stam, C. J. Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin. Neurophysiol. 116, 2266–2301 (2015).
doi: 10.1016/j.clinph.2005.06.011
He, B. J. et al. The temporal structures and functional significance of scale-free brain activity. Neuron 66, 353–369 (2010).
pubmed: 20471349
pmcid: 2878725
doi: 10.1016/j.neuron.2010.04.020
Di Ieva, A. et al. Fractals in the neurosciences, Part I: General principles and basic neurosciences. Neuroscientist 20(4), 403–417 (2014).
pubmed: 24362815
doi: 10.1177/1073858413513927
Di Ieva, A. et al. Fractals in the neurosciences, Part II: Clinical applications and future perspectives. Neuroscientist 21(1), 30–43 (2015).
pubmed: 24362814
doi: 10.1177/1073858413513928
Higuchi, T. Approach to an irregular time series on the basis of the fractal theory. Phys. D 31, 277–283 (1988).
doi: 10.1016/0167-2789(88)90081-4
Inouye, T. et al. Changes in the fractal dimension of alpha-envelope from wakefulness to drowsiness in the human electroencephalogram. Neurosci. Lett. 174(1), 105–108 (1994).
pubmed: 7970142
doi: 10.1016/0304-3940(94)90130-9
Klonowski, W., Olejarczyk, E. & Stepien, R. Complexity of EEG-signal in time domain—Possible biomedical application. AIP Conf. Proc. 622, 155–160 (2002).
doi: 10.1063/1.1487530
Klonowski, W., Olejarczyk, E., Stepien, R., et al. New methods of nonlinear and symbolic dynamics in sleep EEG-signal analysis. In: Modelling and Control in Biomedical Systems 2003 (including Biological Systems). Ed. Feng D., Carson ER. IFAC Symposia series, pp. 241–244, (2003).
Ferenets, R. et al. Comparison of entropy and complexity measures for the assessment of depth of sedation. IEEE Trans. Biomed. Eng. 53(6), 1067–1077 (2006).
pubmed: 16761834
doi: 10.1109/TBME.2006.873543
Kronholm, E. et al. Spectral power and fractal dimension: Methodological comparison in a sample of normal sleepers and chronic insomniacs. Sleep Biol. Rhythms 5(4), 239–250 (2007).
doi: 10.1111/j.1479-8425.2007.00317.x
Olejarczyk, E. Fractal dimension in time domain—application. In EEG-signal analysis Classification and Application of Fractals 161–185 (Nova Science Publishers, 2011).
Croce, P., Quercia, A., Costa, S. & Zappasodi, F. Circadian rhythms in fractal features of EEG signals. Front. Physiol. 9, 1567 (2018).
pubmed: 30483146
pmcid: 6240683
doi: 10.3389/fphys.2018.01567
Klonowski, W., Olejarczyk, E., Stepien, R., et al. Monitoring the Depth of Anaesthesia Using Fractal Complexity Method. in: Complexus Mundi. Emergent Patterns in Nature, (Ed. M.N.Novak), pp. 333–342, (World Scientific, 2006), ISBN 981–256–666-X.
Olejarczyk, E. et al. Evaluation of the EEG-signal during Volatile Anaesthesia: Methodological approach. Biocybern. Biomed. Eng. 29(1), 3–28 (2009).
Spasic, S., Kalauzi, A., Kesic, S., Obradovic, M. & Saponjic, J. Surrogate data modeling the relationship between high frequency amplitudes and Higuchi fractal dimension of EEG signals in anesthetized rats. J. Theor. Biol. 289, 160–166 (2011).
pubmed: 21920374
doi: 10.1016/j.jtbi.2011.08.037
Kuhlmann, L. et al. Tracking electroencephalographic changes using distributions of linear models: Application to propofol-based depth of anesthesia monitoring. IEEE Trans. Biomed. Eng. 64(4), 870–881 (2017).
pubmed: 27323352
doi: 10.1109/TBME.2016.2562261
Truong, Q. D. K., Ha, V. Q. & Toi, V. V. Higuchi fractal properties of onset epilepsy electroencephalogram. Comput. Math. Methods Med. 2012, 461426 (2012).
Choubey, H. & Pandey, A. A combination of statistical parameters for the detection of epilepsy and EEG classification using ANN and KNN classifier. SIViP 15(3), 475–483 (2021).
doi: 10.1007/s11760-020-01767-4
Zappasodi, F. et al. Fractal dimension of EEG activity senses neuronal impairment in acute stroke. PLoS ONE 9(6), e100199 (2014).
pubmed: 24967904
pmcid: 4072666
doi: 10.1371/journal.pone.0100199
Zappasodi, F. et al. Longitudinal quantitative electroencephalographic study in mono-hemispheric stroke patients. Neural Regen. Res. 14(7), 1237–1246 (2019).
pubmed: 30804255
pmcid: 6425833
doi: 10.4103/1673-5374.251331
Bachmann, M., Lass, J., Suhhova, A. & Hinrikus, H. Spectral asymmetry and higuchi’s fractal dimension measures of depression electroencephalogram. Comput. Methods Progr. Biomed. 2013, 251638 (2013).
Lebiecka, K. et al. Complexity analysis of EEG data in persons with depression subjected to transcranial magnetic stimulation. Front. Physiol. 9, 1385 (2018).
pubmed: 30323771
pmcid: 6172427
doi: 10.3389/fphys.2018.01385
Kawe, T. N. J., Shadli, S. M. & McNaughton, N. Higuchi’s fractal dimension, but not frontal or posterior alpha asymmetry, predicts PID-5 anxiousness more than depressivity. Sci. Rep. 9, 19666 (2019).
pubmed: 31873184
pmcid: 6928148
doi: 10.1038/s41598-019-56229-w
Cukic, M. et al. Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression. Int. J. Methods Psychiatr. Res. 29(2), e1816 (2020).
pubmed: 31820528
doi: 10.1002/mpr.1816
Raghavendra, B. S., Dutt, D. N., Halahalli, H. N. & John, J. P. Complexity analysis of EEG in patients with schizophrenia using fractal dimension. Physiol. Meas. 30(8), 795–808 (2009).
pubmed: 19550026
doi: 10.1088/0967-3334/30/8/005
Goshvarpour, A. & Goshvarpour, A. Schizophrenia diagnosis using innovative EEG feature-level fusion schemes. Phys. Eng. Sci. Med. 43(1), 227–238 (2020).
doi: 10.1007/s13246-019-00839-1
Gomez, C., Mediavilla, A., Hornero, R., Abasolo, D. & Fernandez, A. Use of the Higuchi’s fractal dimension for the analysis of MEG recordings from Alzheimer’s disease patients. Med. Eng. Phys. 31(3), 306–313 (2009).
pubmed: 18676171
doi: 10.1016/j.medengphy.2008.06.010
Ahmadlou, M., Adeli, H. & Adeli, A. Fractality and a wavelet-chaos-methodology for EEG-based diagnosis of alzheimer disease. Alzheimer Dis. Assoc. Disord. 25(1), 85–92 (2011).
pubmed: 20811268
doi: 10.1097/WAD.0b013e3181ed1160
Smits, F. M. et al. Electroencephalographic fractal dimension in healthy ageing and Alzheimer’s disease. PlosOne 11(2), e0149587 (2016).
doi: 10.1371/journal.pone.0149587
Zappasodi, F. et al. Age-related changes in electroencephalographic signal complexity. PLoS ONE 10(11), e0141995 (2015).
pubmed: 26536036
pmcid: 4633126
doi: 10.1371/journal.pone.0141995
Klonowski, W., Olejarczyk, E. & Stepien, R. Nonlinear dynamics of EEG-signal reveals influence of magnetic field on the brain. Conf. Proc. IEEE Eng. Med. Biol. Soc. 22, 2955–2958 (2000).
doi: 10.1109/IEMBS.2000.901497
Olejarczyk, E. Application of fractal dimension method of functional MRI time-series to limbic dysregulation in anxiety study. Procedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3408– 3410, (2007).
Rubin, D., Fekete, T. & Mujica-Parodi, L. R. Optimizing complexity measures for fMRI data: Algorithm, artifact, and sensitivity. PlosOne 8(5), e63448 (2013).
doi: 10.1371/journal.pone.0063448
Porcaro, C. et al. Haemodynamic activity characterization of resting state networks by fractal analysis and thalamocortical morphofunctional integrity in chronic migraine. J. Headache Pain 21(1), 112 (2020).
pubmed: 32928129
pmcid: 7490862
doi: 10.1186/s10194-020-01181-8
Cottone, C. et al. Neuronal electrical ongoing activity as a signature of cortical areas. Brain Struct. Funct. 222, 2115–2126 (2017).
pubmed: 27803994
doi: 10.1007/s00429-016-1328-4
Cottone, C. et al. Cortical parcellation based on local neuronal electrical activity. Clin. Neurophysiol. 127, e18–e132 (2016).
doi: 10.1016/j.clinph.2015.11.252
Silva, I. & Moody, G. An open-source toolbox for analysing and processing PhysioNet databases in MATLAB and octave. J. Open Res. Softw. 2(1), e27 (2014).
pubmed: 26525081
pmcid: 4627662
Baker, S. N. Oscillatory interactions between sensorimotor cortex and the periphery. Curr. Opin. Neurobiol. 17, 649–655 (2007).
pubmed: 18339546
pmcid: 2428102
doi: 10.1016/j.conb.2008.01.007
Latreille, V. et al. The human K-complex: Insights from combined scalp-intracranial EEG recordings. Neuroimage 213, 116748 (2020).
pubmed: 32194281
doi: 10.1016/j.neuroimage.2020.116748
Born, A. P. et al. Cortical deactivation induced by visual stimulation in human slow-wave sleep. Neuroimage 17(3), 1325–1335 (2002).
pubmed: 12414272
doi: 10.1006/nimg.2002.1249
Tanaka, H. et al. Effect of stage 1 sleep on auditory cortex during pure tone stimulation: Evaluation by functional magnetic resonance imaging with simultaneous EEG monitoring. Am. J. Neuroradiol. 24(10), 1982–1988 (2003).
pubmed: 14625220
pmcid: 8148913
Olson, I. R., Plotzker, A. & Ezzyat, Y. The Enigmatic temporal pole: a review of findings on social and emotional processing. Brain 130(Pt 7), 1718–1731 (2007).
pubmed: 17392317
doi: 10.1093/brain/awm052
Sun, J. B. et al. Alteration of brain gray matter density after 24 h of sleep deprivation in healthy adults. Front. Neurosci. 14, 754 (2020).
pubmed: 32903801
pmcid: 7438917
doi: 10.3389/fnins.2020.00754
Sritharan, S. Y. et al. Primate somatosensory cortical neurons are entrained to both spontaneous and peripherally evoked spindle oscillations. J. Neurophysiol. 123, 300–307 (2020).
pubmed: 31800329
doi: 10.1152/jn.00471.2019
Vantomme, G. et al. Regulation of local sleep by the thalamic reticular nucleus. Front. Neurosci. 13, 576 (2019).
pubmed: 31231186
pmcid: 6560175
doi: 10.3389/fnins.2019.00576
Bandarabadi, M. et al. A role for spindles in the onset of rapid eye movement sleep. Nat. Commun. 11, 5247 (2020).
pubmed: 33067436
pmcid: 7567828
doi: 10.1038/s41467-020-19076-2
Steriade, M. & Llinas, R. R. The functional states of the thalamus and the associated neuronal interplay. Physiol. Rev. 68(3), 649–742 (1988).
pubmed: 2839857
doi: 10.1152/physrev.1988.68.3.649
Coulon, P., Budde, T. & Pape, H. The sleep relay-the role of the thalamus in central and decentral sleep regulation. Pflugers Arch 463, 53–71 (2012).
pubmed: 21912835
doi: 10.1007/s00424-011-1014-6