Simultaneous fMRI-EEG-Based Characterisation of NREM Parasomnia Disease: Methods and Limitations
Status PubMed-not-MEDLINE Jazyk angličtina Země Švýcarsko Médium electronic
Typ dokumentu časopisecké články
PubMed
33327626
PubMed Central
PMC7765133
DOI
10.3390/diagnostics10121087
PII: diagnostics10121087
Knihovny.cz E-zdroje
- Klíčová slova
- EEG, NREM parasomnia, disorders of arousal, fMRI, simultaneous measurement,
- Publikační typ
- časopisecké články MeSH
Functional magnetic resonance imaging (fMRI) techniques and electroencephalography (EEG) were used to investigate sleep with a focus on impaired arousal mechanisms in disorders of arousal (DOAs). With a prevalence of 2-4% in adults, DOAs are significant disorders that are currently gaining attention among physicians. The paper describes a simultaneous EEG and fMRI experiment conducted in adult individuals with DOAs (n=10). Both EEG and fMRI data were validated by reproducing well established EEG and fMRI associations. A method for identification of both brain functional areas and EEG rhythms associated with DOAs in shallow sleep was designed. Significant differences between patients and controls were found in delta, theta, and alpha bands during awakening epochs. General linear models of the blood-oxygen-level-dependent signal have shown the secondary visual cortex and dorsal posterior cingulate cortex to be associated with alpha spectral power fluctuations, and the precuneus with delta spectral power fluctuations, specifically in patients and not in controls. Future EEG-fMRI sleep studies should also consider subject comfort as an important aspect in the experimental design.
3rd Faculty of Medicine Charles University 10000 Prague Czech Republic
Institute of Computer Science of the Czech Academy of Sciences 18207 Prague Czech Republic
National Institute of Mental Health 25067 Klecany Czech Republic
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Panossian L.A., Avidan A.Y. Review of Sleep Disorders. Med. Clin. N. Am. 2009;93:407–425. doi: 10.1016/j.mcna.2008.09.001. PubMed DOI
American Academy of Sleep Medicine . The International Classification of Sleep Disorders: Diagnostic and Coding Manual. 2nd ed. American Academy of Sleep Medicine; Westchester, NY, USA: 2005.
Lopez R., Jaussent I., Scholz S., Bayard S., Montplaisir J., Dauvilliers Y. Functional Impairment in Adult Sleepwalkers: A Case-Control Study. Sleep. 2013;36:345–351. doi: 10.5665/sleep.2446. PubMed DOI PMC
Januszko P., Niemcewicz S., Gajda T., Wołynczyk-Gmaj D., Piotrowska A.J., Gmaj B., Piotrowski T., Szelenberger W. Sleepwalking episodes are preceded by arousal-related activation in the cingulate motor area: EEG current density imaging. Clin. Neurophysiol. 2016;127:530–536. doi: 10.1016/j.clinph.2015.01.014. PubMed DOI
Desjardins M.-E., Carrier J., Lina J.-M., Fortin M., Gosselin N., Montplaisir J., Zadra A. EEG Functional Connectivity Prior to Sleepwalking. Sleep. 2017;40:zsx024. doi: 10.1093/sleep/zsx024. PubMed DOI PMC
Pressman M.R., Mahowald M., Schenck C., Bornemann M.C., Banerjee D., Howell M., Buchanan P., Avidan A. Spectral EEG Analysis and Sleepwalking Defense. J. Clin. Sleep Med. 2014;10:111–112. doi: 10.5664/jcsm.3380. PubMed DOI PMC
Espa F., Ondze B., Deglise P., Billiard M., Besset A. Sleep architecture, slow wave activity, and sleep spindles in adult patients with sleepwalking and sleep terrors. Clin. Neurophysiol. 2000;111:929–939. doi: 10.1016/S1388-2457(00)00249-2. PubMed DOI
Gaudreau H., Joncas S., Zadra A., Montplaisir J.Y. Dynamics of slow-wave activity during the NREM sleep of sleepwalkers and control subjects. Sleep. 2000;23:755–760. doi: 10.1093/sleep/23.6.1d. PubMed DOI
Jaar O., Pilon M., Carrier J., Montplaisir J., Zadra A. Analysis of Slow-Wave Activity and Slow-Wave Oscillations Prior to Somnambulism. Sleep. 2010;33:1511–1516. doi: 10.1093/sleep/33.11.1511. PubMed DOI PMC
Castelnovo A., Riedner B.A., Smith R.F., Tononi G., Boly M., Benca R.M. Scalp and Source Power Topography in Sleepwalking and Sleep Terrors: A High-Density EEG Study. Sleep. 2016;39:1815–1825. doi: 10.5665/sleep.6162. PubMed DOI PMC
Heidbreder A., Stefani A., Brandauer E., Steiger R., Kremser C., Gizewski E.R., Young P., Poewe W., Högl B., Scherfler C. Gray matter abnormalities of the dorsal posterior cingulate in sleep walking. Sleep Med. 2017;36:152–155. doi: 10.1016/j.sleep.2017.05.007. PubMed DOI
Cavanna A.E., Shah S., Eddy C.M., Williams A.E., Rickards H.E. Consciousness: A Neurological Perspective. Behav. Neurol. 2011;24:107–116. doi: 10.1155/2011/645159. PubMed DOI PMC
Kaufmann C. Brain activation and hypothalamic functional connectivity during human non-rapid eye movement sleep: An EEG/fMRI study. Brain. 2006;129:655–667. doi: 10.1093/brain/awh686. PubMed DOI
Horovitz S.G., Fukunaga M., de Zwart J.A., van Gelderen P., Fulton S.C., Balkin T.J., Duyn J.H. Low frequency BOLD fluctuations during resting wakefulness and light sleep: A simultaneous EEG-fMRI study. Hum. Brain Mapp. 2008;29:671–682. doi: 10.1002/hbm.20428. PubMed DOI PMC
Portas C.M., Krakow K., Allen P., Josephs O., Armony J.L., Frith C.D. Auditory Processing across the Sleep-Wake Cycle. Neuron. 2000;28:991–999. doi: 10.1016/S0896-6273(00)00169-0. PubMed DOI
Moehlman T.M., de Zwart J.A., Chappel-Farley M.G., Liu X., McClain I.B., Chang C., Mandelkow H., Ozbay P.S., Johnson N.L., Bieber R.E., et al. All-night functional magnetic resonance imaging sleep studies. J. Neurosci. Methods. 2019;316:83–98. doi: 10.1016/j.jneumeth.2018.09.019. PubMed DOI PMC
Duyn J.H. EEG-fMRI Methods for the Study of Brain Networks during Sleep. Front. Neurol. 2012;3:1664–2295. doi: 10.3389/fneur.2012.00100. PubMed DOI PMC
Clark C., Lawrence R., Brown G. Sleep Deprivation, EEG, and Functional MRI in Depression Preliminary Results. Neuropsychopharmacology. 2001;25:79–84. doi: 10.1016/S0893-133X(01)00324-4. PubMed DOI
Horovitz S.G., Braun A.R., Carr W.S., Picchioni D., Balkin T.J., Fukunaga M., Duyn J.H. Decoupling of the brain’s default mode network during deep sleep. Proc. Natl. Acad. Sci. USA. 2009;106:11376–11381. doi: 10.1073/pnas.0901435106. PubMed DOI PMC
Hong C.H., Harris J.C., Pearlson G.D., Kim J.S., Calhoun V.D., Fallon J.H., Golay X., Gillen J.S., Simmonds D.J., van Zijl P.C.M., et al. FMRI evidence for multisensory recruitment associated with rapid eye movements during sleep. Hum. Brain Mapp. 2009;30:1705–1722. doi: 10.1002/hbm.20635. PubMed DOI PMC
Chowdhury M.E.H., Mullinger K.J., Bowtell R. Simultaneous EEG–fMRI: Evaluating the effect of the cabling configuration on the gradient artefact. Phys. Med. Biol. 2015;60:241–250. doi: 10.1088/0031-9155/60/12/N241. PubMed DOI
Allen P.J., Josephs O., Turner R. A Method for Removing Imaging Artifact from Continuous EEG Recorded during Functional MRI. NeuroImage. 2000;12:230–239. doi: 10.1006/nimg.2000.0599. PubMed DOI
Grouiller F., Vercueil L., Krainik A., Segebarth C., Kahane P., David O. A comparative study of different artefact removal algorithms for EEG signals acquired during functional MRI. NeuroImage. 2007;38:124–137. doi: 10.1016/j.neuroimage.2007.07.025. PubMed DOI
Negishi M., Abildgaard M., Nixon T., Constable R.T. Removal of time-varying gradient artifacts from EEG data acquired during continuous fMRI. Clin. Neurophysiol. 2004;115:2181–2192. doi: 10.1016/j.clinph.2004.04.005. PubMed DOI
Abreu R., Leite M., Jorge J., Grouiller F., van der Zwaag W., Leal A., Figueiredo P. Ballistocardiogram artifact correction taking into account physiological signal preservation in simultaneous EEG-fMRI. NeuroImage. 2016;135:45–63. doi: 10.1016/j.neuroimage.2016.03.034. PubMed DOI
Niazy R.K., Iannetti G., Beckmann C.F., Brady M., Smith S.M. Improved FMRI Artifact Reduction from Simultaneously Acquired EEG Data Using Slice Dependant Template Matching. ISMR; Kyoto, Japan: 2004.
Marino M., Liu Q., Koudelka V., Porcaro C., Hlinka J., Wenderoth N., Mantini D. Adaptive optimal basis set for BCG artifact removal in simultaneous EEG-fMRI. Sci. Rep. 2018;8:8902. doi: 10.1038/s41598-018-27187-6. PubMed DOI PMC
Piorecky M., Koudelka V., Strobl J., Brunovsky M., Krajca V. Artifacts in Simultaneous hdEEG/fMRI Imaging: A Nonlinear Dimensionality Reduction Approach. Sensors. 2019;19:4454. doi: 10.3390/s19204454. PubMed DOI PMC
Jung T.P., Humphries C., Lee T.W., Makeig S., McKeown M.J., Iragui V., Sejnowski T.J. Removing Electroencephalographic Artifacts: Comparison between ICA and PCA; Proceedings of the Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop; Cambridge, UK. 2 September 1998; pp. 63–72.
Oostenveld R., Fries P., Maris E., Schoffelen J.-M. FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. Comput. Intell. Neurosci. 2011;2011:156869. doi: 10.1155/2011/156869. PubMed DOI PMC
GNU General Public Licence SPM12. 1 October 2014. [(accessed on 16 September 2020)]; Available online: https://www.fil.ion.ucl.ac.uk/spm/software/spm12.
Babadi B., Brown E.N. A Review of Multitaper Spectral Analysis. IEEE Trans. Biomed. Eng. 2014;61:1555–1564. doi: 10.1109/TBME.2014.2311996. PubMed DOI
Wei W.W.S. Multivariate Time Series Analysis and Applications. John Wiley & Sons, Ltd.; Chichester, UK: 2019.
Carrier J., Land S., Buysse D.J., Kupfer D.J., Monk T.H. The effects of age and gender on sleep EEG power spectral density in the middle years of life (ages 20–60 years old) Psychophysiology. 2001;38:232–242. doi: 10.1111/1469-8986.3820232. PubMed DOI
Maris E., Oostenveld R. Nonparametric statistical testing of EEG- and MEG-data. J. Neurosci. Methods. 2007;164:177–190. doi: 10.1016/j.jneumeth.2007.03.024. PubMed DOI
Jahankhani P., Revett K., Kodogiannis V. Data Mining an EEG Dataset With an Emphasis on Dimensionality Reduction; Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining; Honolulu, HI, USA. 1 March–5 April 2007; pp. 405–412.
García-Laencina P.J., Rodríguez-Bermudez G., Roca-Dorda J. Exploring dimensionality reduction of EEG features in motor imagery task classification. Expert Syst. Appl. 2014;41:5285–5295. doi: 10.1016/j.eswa.2014.02.043. DOI
Birjandtalab J., Pouyan M.B., Cogan D., Nourani M., Harvey J. Automated seizure detection using limited-channel EEG and non-linear dimension reduction. Comput. Biol. Med. 2017;82:49–58. doi: 10.1016/j.compbiomed.2017.01.011. PubMed DOI
Piorecky M., Cerna E., Piorecka V., Krajca V., Koudelka V. World Congress on Medical Physics and Biomedical Engineering. Volume 68. Springer; Berlin/Heidelberg, Germany: 2018. Simulation, Modification and Dimension Reduction of EEG Feature Space; pp. 425–429.
Zhang Y., Xu G., Wang J., Liang L. An automatic patient-specific seizure onset detection method in intracranial EEG based on incremental nonlinear dimensionality reduction. Comput. Biol. Med. 2010;40:889–899. doi: 10.1016/j.compbiomed.2010.09.010. PubMed DOI
Cui X., Li J., Song X., Ma Z. XjView: A Viewing Program for SPM; Alivelearn. [(accessed on 16 September 2020)];2020 Available online: https://www.alivelearn.net/xjview.
Vetrugno R., Provini F., Meletti S., Plazzi G., Liguori R., Cortelli P., Lugaresi E., Montagna P. Propriospinal Myoclonus at the Sleep-Wake Transition: A New Type of Parasomnia. Sleep. 2001;24:1550–9109. PubMed
Espinar J. Alertness disorders and parasomnias of the wakefulness-sleep transition. Rev. Neurol. 1998;26:469–472. PubMed
Lengler U., Kafadar I., Neubauer B.A., Krakow K. fMRI correlates of interictal epileptic activity in patients with idiopathic benign focal epilepsy of childhood: A simultaneous EEG–functional MRI study. Epilepsy Res. 2007;75:29–38. doi: 10.1016/j.eplepsyres.2007.03.016. PubMed DOI
Ritter P., Moosmann M., Villringer A. Rolandic alpha and beta EEG rhythms’ strengths are inversely related to fMRI-BOLD signal in primary somatosensory and motor cortex. Hum. Brain Mapp. 2009;30:1168–1187. doi: 10.1002/hbm.20585. PubMed DOI PMC
Hanslmayr S., Volberg G., Wimber M., Raabe M., Greenlee M.W., Bauml K.-H.T. The Relationship between Brain Oscillations and BOLD Signal during Memory Formation: A Combined EEG–fMRI Study. J. Neurosci. 2011;31:15674–15680. doi: 10.1523/JNEUROSCI.3140-11.2011. PubMed DOI PMC
Davey J., Thompson H.E., Hallam G., Karapanagiotidis T., Murphy C., De Caso I., Krieger-Redwood K., Bernhardt B.C., Smallwood J., Jefferies E. Exploring the role of the posterior middle temporal gyrus in semantic cognition: Integration of anterior temporal lobe with executive processes. NeuroImage. 2016;137:165–177. doi: 10.1016/j.neuroimage.2016.05.051. PubMed DOI PMC
Koechlin E., Hyafil A. Anterior Prefrontal Function and the Limits of Human Decision-Making. Science. 2019;318:594–598. doi: 10.1126/science.1142995. PubMed DOI
Chua P., Dolan R.J. The Neurobiology of Anxiety and Anxiety-Related Disorders: A Functional Neuroimaging Perspective. Elsevier; Amsterdam, The Netherlands: 2000. pp. 509–522.
Goetz C.G. Textbook of Clinical Neurology. Elsevier; Amsterdam, The Netherlands: 2007.
Dronkers N.F., Wilkins D.P., Van Valin R.D., Redfern B.B., Jaege J.J. Lesion analysis of the brain areas involved in language comprehension. Cognition. 2004;92:145–177. doi: 10.1016/j.cognition.2003.11.002. PubMed DOI
Strangman G., Thompson J.H., Strauss M.M., Marshburn T.H., Sutton J.P. Functional brain imaging of a complex navigation task following one night of total sleep deprivation. J. Sleep Res. Prelim. Study. 2005;14:369–375. doi: 10.1111/j.1365-2869.2005.00488.x. PubMed DOI
Stein J. Reference Module in Neuroscience and Biobehavioral Psychology. Sensorimotor Control. Elsevier; Amsterdam, The Netherlands: 2017.
Hrozanova M., Morrison I., Riha R. Adult NREM Parasomnias: An Update. Clocks Sleep. 2018;1:87–104. doi: 10.3390/clockssleep1010009. PubMed DOI PMC
Hyvarinen J. The Parietal Cortex of Monkey and Man. Springer; Berlin/Heidelberg, Germany: 1982. Symptoms of Posterior Parietal Lesions.
Joseph R.G. Neuropsychiatry, Neuropsychology, and Clinical Neuroscience: Emotion, Evolution, Cognition, Language, Memory, Brain Damage, and Abnormal Behavior. Williams & Wilkins; Philadelphia, PA, USA: 1996. Parietal area 7: Visual spatial capabilities.