Hyper-Frequency Network Topology Changes During Choral Singing.
Identifieur interne : 000055 ( Main/Exploration ); précédent : 000054; suivant : 000056Hyper-Frequency Network Topology Changes During Choral Singing.
Auteurs : Viktor Müller [Allemagne] ; Julia A M. Delius [Allemagne] ; Ulman Lindenberger [Allemagne, Royaume-Uni]Source :
- Frontiers in physiology [ 1664-042X ] ; 2019.
Abstract
Choral singing requires the coordination of physiological subsystems within and across individuals. Previously, we suggested that the choir functions as a superordinate system that imposes boundary conditions on the dynamic features of the individual singers and found reliable differences in the network topography by analyzing within- and cross-frequency couplings (WFC and CFC, respectively). Here, we further refine our analyses to investigate hyper-frequency network (HFN) topology structures (i.e., the layout or arrangement of connections) using a graph-theoretical approach. In a sample of eleven singers and one conductor engaged in choral singing (aged between 23 and 56 years, and including five men and seven women), we calculated phase coupling (WFC and CFC) between respiratory, cardiac, and vocalizing subsystems across ten frequencies of interest. All these couplings were used for construction of HFN with nodes being a combination of frequency components and subsystems across choir participants. With regard to the network topology measures, we found that clustering coefficients (CCs) as well as local and global efficiency were highest and characteristic path lengths, correspondingly, were shortest when the choir sang a canon in parts as compared to singing it in unison. Furthermore, these metrics revealed a significant relationship to individual heart rate, as an indicator of arousal, and to an index of heart rate variability indicated by the LF/HF ratio (low and high frequency, respectively), and reflecting the balance between sympathetic and parasympathetic activity. In addition, we found that the CC and local efficiency for groups singing the same canon part were higher than for groups of singers constructed randomly post hoc, indicating stronger neighbor-neighbor connections in the former. We conclude that network topology dynamics are a crucial determinant of group behavior and may represent a potent biomarker for social interaction.
DOI: 10.3389/fphys.2019.00207
PubMed: 30899229
PubMed Central: PMC6416178
Affiliations:
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Delius</name><affiliation wicri:level="3"><nlm:affiliation>Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.</nlm:affiliation><country xml:lang="fr">Allemagne</country><wicri:regionArea>Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin</wicri:regionArea><placeName><region type="land" nuts="3">Berlin</region><settlement type="city">Berlin</settlement></placeName></affiliation></author><author><name sortKey="Lindenberger, Ulman" sort="Lindenberger, Ulman" uniqKey="Lindenberger U" first="Ulman" last="Lindenberger">Ulman Lindenberger</name><affiliation wicri:level="3"><nlm:affiliation>Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.</nlm:affiliation><country xml:lang="fr">Allemagne</country><wicri:regionArea>Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin</wicri:regionArea><placeName><region type="land" nuts="3">Berlin</region><settlement type="city">Berlin</settlement></placeName></affiliation><affiliation wicri:level="3"><nlm:affiliation>Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, United Kingdom.</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London</wicri:regionArea><placeName><settlement type="city">Londres</settlement><region type="country">Angleterre</region><region type="région" nuts="1">Grand Londres</region></placeName></affiliation><affiliation wicri:level="3"><nlm:affiliation>Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany.</nlm:affiliation><country xml:lang="fr">Allemagne</country><wicri:regionArea>Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin</wicri:regionArea><placeName><region type="land" nuts="3">Berlin</region><settlement type="city">Berlin</settlement></placeName></affiliation></author></analytic><series><title level="j">Frontiers in physiology</title><idno type="ISSN">1664-042X</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Choral singing requires the coordination of physiological subsystems within and across individuals. Previously, we suggested that the choir functions as a superordinate system that imposes boundary conditions on the dynamic features of the individual singers and found reliable differences in the network topography by analyzing within- and cross-frequency couplings (WFC and CFC, respectively). Here, we further refine our analyses to investigate hyper-frequency network (HFN) topology structures (i.e., the layout or arrangement of connections) using a graph-theoretical approach. In a sample of eleven singers and one conductor engaged in choral singing (aged between 23 and 56 years, and including five men and seven women), we calculated phase coupling (WFC and CFC) between respiratory, cardiac, and vocalizing subsystems across ten frequencies of interest. All these couplings were used for construction of HFN with nodes being a combination of frequency components and subsystems across choir participants. With regard to the network topology measures, we found that clustering coefficients (<i>CC</i>s) as well as local and global efficiency were highest and characteristic path lengths, correspondingly, were shortest when the choir sang a canon in parts as compared to singing it in unison. Furthermore, these metrics revealed a significant relationship to individual heart rate, as an indicator of arousal, and to an index of heart rate variability indicated by the <i>LF/HF</i> ratio (low and high frequency, respectively), and reflecting the balance between sympathetic and parasympathetic activity. In addition, we found that the <i>CC</i> and local efficiency for groups singing the same canon part were higher than for groups of singers constructed randomly <i>post hoc</i>, indicating stronger neighbor-neighbor connections in the former. We conclude that network topology dynamics are a crucial determinant of group behavior and may represent a potent biomarker for social interaction.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">30899229</PMID><DateRevised><Year>2020</Year><Month>10</Month><Day>01</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Print">1664-042X</ISSN><JournalIssue CitedMedium="Print"><Volume>10</Volume><PubDate><Year>2019</Year></PubDate></JournalIssue><Title>Frontiers in physiology</Title></Journal><ArticleTitle>Hyper-Frequency Network Topology Changes During Choral Singing.</ArticleTitle><Pagination><MedlinePgn>207</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3389/fphys.2019.00207</ELocationID><Abstract><AbstractText>Choral singing requires the coordination of physiological subsystems within and across individuals. Previously, we suggested that the choir functions as a superordinate system that imposes boundary conditions on the dynamic features of the individual singers and found reliable differences in the network topography by analyzing within- and cross-frequency couplings (WFC and CFC, respectively). Here, we further refine our analyses to investigate hyper-frequency network (HFN) topology structures (i.e., the layout or arrangement of connections) using a graph-theoretical approach. In a sample of eleven singers and one conductor engaged in choral singing (aged between 23 and 56 years, and including five men and seven women), we calculated phase coupling (WFC and CFC) between respiratory, cardiac, and vocalizing subsystems across ten frequencies of interest. All these couplings were used for construction of HFN with nodes being a combination of frequency components and subsystems across choir participants. With regard to the network topology measures, we found that clustering coefficients (<i>CC</i>s) as well as local and global efficiency were highest and characteristic path lengths, correspondingly, were shortest when the choir sang a canon in parts as compared to singing it in unison. Furthermore, these metrics revealed a significant relationship to individual heart rate, as an indicator of arousal, and to an index of heart rate variability indicated by the <i>LF/HF</i> ratio (low and high frequency, respectively), and reflecting the balance between sympathetic and parasympathetic activity. In addition, we found that the <i>CC</i> and local efficiency for groups singing the same canon part were higher than for groups of singers constructed randomly <i>post hoc</i>, indicating stronger neighbor-neighbor connections in the former. We conclude that network topology dynamics are a crucial determinant of group behavior and may represent a potent biomarker for social interaction.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Müller</LastName><ForeName>Viktor</ForeName><Initials>V</Initials><AffiliationInfo><Affiliation>Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Delius</LastName><ForeName>Julia A M</ForeName><Initials>JAM</Initials><AffiliationInfo><Affiliation>Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lindenberger</LastName><ForeName>Ulman</ForeName><Initials>U</Initials><AffiliationInfo><Affiliation>Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, United Kingdom.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>03</Month><Day>06</Day></ArticleDate></Article><MedlineJournalInfo><Country>Switzerland</Country><MedlineTA>Front Physiol</MedlineTA><NlmUniqueID>101549006</NlmUniqueID><ISSNLinking>1664-042X</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">cardiac and respiratory autonomic responses</Keyword><Keyword MajorTopicYN="N">cross-frequency coupling</Keyword><Keyword MajorTopicYN="N">graph-theoretic approach</Keyword><Keyword MajorTopicYN="N">heart rate variability</Keyword><Keyword MajorTopicYN="N">interpersonal action coordination</Keyword><Keyword MajorTopicYN="N">social networks</Keyword><Keyword MajorTopicYN="N">within-frequency coupling</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2018</Year><Month>10</Month><Day>31</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2019</Year><Month>02</Month><Day>18</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2019</Year><Month>3</Month><Day>23</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2019</Year><Month>3</Month><Day>23</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2019</Year><Month>3</Month><Day>23</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId 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Delius</name><name sortKey="Lindenberger, Ulman" sort="Lindenberger, Ulman" uniqKey="Lindenberger U" first="Ulman" last="Lindenberger">Ulman Lindenberger</name><name sortKey="Lindenberger, Ulman" sort="Lindenberger, Ulman" uniqKey="Lindenberger U" first="Ulman" last="Lindenberger">Ulman Lindenberger</name></country><country name="Royaume-Uni"><region name="Angleterre"><name sortKey="Lindenberger, Ulman" sort="Lindenberger, Ulman" uniqKey="Lindenberger U" first="Ulman" last="Lindenberger">Ulman Lindenberger</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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