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

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.

SUBMITTER: Haartsen R

PROVIDER: S-EPMC7391718 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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

Haartsen Rianne R van der Velde Bauke B Jones Emily J H EJH Johnson Mark H MH Kemner Chantal C

Scientific reports 20200729 1

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 ...[more]

PMID: 32728099

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Project description:BackgroundIn combined with neurofeedback, Motor Imagery (MI) based Brain-Computer Interface (BCI) has been an effective long-term treatment therapy for motor dysfunction caused by neurological injury in the brain (e.g., post-stroke hemiplegia). However, individual neurological differences have led to variability in the single sessions of rehabilitation training. Research on the impact of short training sessions on brain functioning patterns can help evaluate and standardize the short duration of rehabilitation training. In this paper, we use the electroencephalogram (EEG) signals to explore the brain patterns' changes after a short-term rehabilitation training.Materials and methodsUsing an EEG-BCI system, we analyzed the changes in short-term (about 1-h) MI training data with and without visual feedback, respectively. We first examined the EEG signal's Mu band power's attenuation caused by Event-Related Desynchronization (ERD). Then we use the EEG's Event-Related Potentials (ERP) features to construct brain networks and evaluate the training from multiple perspectives: small-scale based on single nodes, medium-scale based on hemispheres, and large-scale based on all-brain.ResultsResults showed no significant difference in the ERD power attenuation estimation in both groups. But the neurofeedback group's ERP brain network parameters had substantial changes and trend properties compared to the group without feedback. The neurofeedback group's Mu band power's attenuation increased but not significantly (fitting line slope = 0.2, t-test value p > 0.05) after the short-term MI training, while the non-feedback group occurred an insignificant decrease (fitting line slope = -0.4, t-test value p > 0.05). In the ERP-based brain network analysis, the neurofeedback group's network parameters were attenuated in all scales significantly (t-test value: p < 0.01); while the non-feedback group's most network parameters didn't change significantly (t-test value: p > 0.05).ConclusionThe MI-BCI training's short-term effects does not show up in the ERD analysis significantly but can be detected by ERP-based network analysis significantly. Results inspire the efficient evaluation of short-term rehabilitation training and provide a useful reference for subsequent studies.

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

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

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