{"corpus_id":91749025,"paper_sha":"f11abda0fe99ba0ff3b97de7518957f28c927093","doi":"10.1371/journal.pcbi.1007055","arxiv_id":null,"pmid":31086368,"pmcid":"6534335","mag_id":2951757968,"dblp_id":"journals/ploscb/SchaworonkowN19","acl_id":null,"title":"Spatial neuronal synchronization and the waveform of oscillations: Implications for EEG and MEG","year":2018,"publication_date":"2018-08-27","venue":"bioRxiv","journal":{"name":"PLoS Computational Biology","pages":null,"volume":"15"},"journal_issn":null,"journal_title":null,"publication_types":["JournalArticle"],"pubmed_pub_types":["Journal Article","Research Support, Non-U.S. Gov't"],"s2_fields_of_study":["Biology","Medicine","Physics","Computer Science"],"reference_count":69,"citation_count":44,"influential_citation_count":1,"is_open_access":true,"arxiv_categories":null,"arxiv_license":null,"arxiv_journal_ref":null,"mesh_headings":[{"d":"Adult","mj":false,"ui":"D000328"},{"d":"Aged","mj":false,"ui":"D000368"},{"d":"Brain","mj":false,"qs":[{"q":"physiology","mj":false,"ui":"Q000502"}],"ui":"D001921"},{"d":"Brain Mapping","mj":false,"qs":[{"q":"statistics & numerical data","mj":false,"ui":"Q000706"}],"ui":"D001931"},{"d":"Brain Waves","mj":false,"qs":[{"q":"physiology","mj":true,"ui":"Q000502"}],"ui":"D058256"},{"d":"Computational Biology","mj":false,"ui":"D019295"},{"d":"Computer Simulation","mj":false,"ui":"D003198"},{"d":"Electroencephalography","mj":false,"qs":[{"q":"statistics & numerical data","mj":true,"ui":"Q000706"}],"ui":"D004569"},{"d":"Electroencephalography Phase Synchronization","mj":false,"ui":"D058407"},{"d":"Female","mj":false,"ui":"D005260"},{"d":"Humans","mj":false,"ui":"D006801"},{"d":"Magnetoencephalography","mj":false,"qs":[{"q":"statistics & numerical data","mj":true,"ui":"Q000706"}],"ui":"D015225"},{"d":"Male","mj":false,"ui":"D008297"},{"d":"Middle Aged","mj":false,"ui":"D008875"},{"d":"Models, Neurological","mj":false,"ui":"D008959"},{"d":"Neurons","mj":false,"qs":[{"q":"physiology","mj":false,"ui":"Q000502"}],"ui":"D009474"},{"d":"Signal Processing, Computer-Assisted","mj":false,"ui":"D012815"},{"d":"Signal-To-Noise Ratio","mj":false,"ui":"D059629"},{"d":"Young Adult","mj":false,"ui":"D055815"}],"chemicals":null,"comments_corrections":null,"source_flags":5,"s2_open_access_pdf_url":"https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007055&type=printable","s2_open_access_landing_url":"https://www.semanticscholar.org/paper/f11abda0fe99ba0ff3b97de7518957f28c927093","s2_open_access_license":"CCBY","s2_open_access_status":"GOLD","pmc_open_access_pdf_url":null,"pmc_open_access_landing_url":null,"pmc_open_access_license":null,"pmc_open_access_status":null,"unpaywall_open_access_pdf_url":null,"unpaywall_open_access_landing_url":null,"unpaywall_open_access_license":null,"unpaywall_open_access_status":null,"abstract":"Neuronal oscillations are ubiquitous in the human brain and are implicated in virtually all brain functions. Often they are referred to by their frequency content, i.e., α-, β-, γ-oscillations. Although they indeed can be described by a prominent peak in the power spectrum, their waveform is not necessarily sinusoidal and shows a rather complex morphology which needs to be captured with multiple spectral harmonics. Both frequency and temporal descriptions of such non-sinusoidal neuronal oscillations can be utilized. However, in non-invasive EEG/MEG recordings the waveform of oscillations often takes a sinusoidal shape which in turn leads to a rather oversimplified view on oscillatory processes. In this study, we show in simulations how spatial synchronization can mask non-sinusoidal features of the underlying rhythmic neuronal processes. Consequently, the degree of non-sinusoidality can serve as a measure of spatial synchronization. To confirm this empirically, we show that a mixture of EEG components is indeed associated with more sinusoidal oscillations compared to the waveform of oscillations in each constituent component. Using simulations, we also show that the spatial mixing of the non-sinusoidal neuronal signals strongly affects the amplitude ratio of the spectral harmonics constituting the waveform. This in turn has high relevance for the interpretation of the relative strength of spectral peaks, which is commonly used for inferring neuronal signatures corresponding to specific behavioral states. Moreover, our simulations show how spatial mixing can affect the strength and even the direction of the amplitude coupling between constituent neuronal harmonics. Consistently with these simulations, we also demonstrate these effects in real EEG recordings. Our findings have far reaching implications for the neu-rophysiological interpretation of neuronal oscillations and cross-frequency interactions, as well as for the unequivocal determination of oscillatory phase.","claims":[{"public_id":"cl_c5272f3c416896db1e012bd0bee5505e","status":"active","text":"A mixture of EEG components is associated with more sinusoidal oscillations compared to the waveform of oscillations in each constituent component.","confidence":0.9,"contributors":[{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["extraction"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["review"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_c5272f3c416896db1e012bd0bee5505e"},{"public_id":"cl_e3b46ad36401a9cb9565dc5af6f45af1","status":"active","text":"Spatial mixing can affect the strength and direction of amplitude coupling between constituent 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