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.
Highlights
Neurological and psychiatric disorders have been associated with disruptions or atypicalities in brain n etworks[1]
Reliabilities were within the poor range for 20 and 30 1- and 2-s epochs (0 ≤ ICCPLI ≤ 0.14, 0 ≤ ICCdbWPLI ≤ 0.24), and in the good and excellent ranges for 50 and 60 4- and 5-s epochs (0.60 ≤ ICCPLI ≤ 0.87, 0.62 ≤ ICCdbWPLI ≤ 0.85)
We found that whole brain connectivity was a more reliable metric than graph theory metrics
Summary
Neurological and psychiatric disorders have been associated with disruptions or atypicalities in brain n etworks[1]. Given that neural connectivity at 12 months predicts repetitive behaviours at age 2 21,27, individual differences in infant brain connectivity should exhibit a degree of stability within individuals. At least some degree of persistence over time would likely be necessary for either the individual differences in connectivity to underpin differences in behaviour at the later timepoint, or for individual differences measured in infants with a relatively heterogenous age span to have sufficient predictive value for later behaviour This is relevant for developmental studies in neurodevelopmental disorders who aim to identify early factors of atypical development and examine the stability of these factors across different time windows during infancy and toddlerhood (e.g.30). We ask: can we reliably measure brain networks in infants at a similar interval? What network characteristics can we measure reliably? How can we measure these characteristics in an optimal way?
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