Using multiple short epochs optimises the stability of infant EEG connectivity parameters

R. Haartsen*, B. van der Velde, E.J.H. Jones, M.H. Johnson, C. Kemner

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Atypicalities in connectivity between brain regions have been implicated in a range of neurocognitive disorders. We require metrics to assess stable individual differences in connectivity in the developing brain, while facing the challenge of limited data quality and quantity. Here, we examine how varying core processing parameters can optimise the test–retest reliability of EEG connectivity measures in infants. EEG was recorded twice with a 1-week interval between sessions in 10-month-olds. EEG alpha connectivity was measured across different epoch lengths and numbers, with the phase lag index (PLI) and debiased weighted PLI (dbWPLI), for both whole-head connectivity and graph theory metrics. We calculated intra-class correlations between sessions for infants with sufficient data for both sessions (N’s = 19–41, depending on the segmentation method). Reliability for the whole brain dbWPLI was higher across many short epochs, whereas reliability for the whole brain PLI was higher across fewer long epochs. However, the PLI is confounded by the number of available segments. Reliability was higher for whole brain connectivity than graph theory metrics. Thus, segmenting available data into a high number of short epochs and calculating the dbWPLI is most appropriate for characterising connectivity in populations with limited availability of EEG data.
Original languageEnglish
Pages (from-to)12703-12716
Number of pages13
JournalScientific Reports
Volume10
Issue number1
DOIs
Publication statusPublished - 2020

Keywords

  • EEG
  • Infant
  • Graph Theory
  • Brain Network
  • Reliability

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