Poster #18 - Inter-individual variability during neurodevelopment; an investigation in a homogenous group of infants

• In Event: Poster Session 14

Abstract

Interests in using electroencephalography (EEG) features in developmental neuroscience has been extensively increasing in recent decades as potentially reliable clinical markers of brain function, particularly in infants. The features extracted from brain activities recorded by EEG are assessed statistically on the basis of populations’ average to recognize a particular and prominent behavior. It is assumed that despite some subject-specific differences in individual’s brain signals, the mean value of an EEG feature can be representative of the whole population with identical neurological condition (e.g. healthy subjects and diagnosable neurological patients). However, there can be large variability within a population even a homogenous one (e.g. the same age and pathological condition), making comparisons difficult. With this high inter-individual variability and also low numbers of trials and low signal-to-noise ratios in pediatric populations, establishing reliable biomarkers can be difficult in practice. One approach is to identify EEG features that show less inter-individual variability and are relatively stable in a healthy population during development. To this aim, we introduce an innovative application of statistical measures of variance for the analysis of RS- EEG data in a group of 6-month infants. Using Coefficient of dispersion based on MAD, Quantile coefficient of dispersion (QCD), Mean- median (MM), Dispersion and modified dispersion ratio, Rousseeuw & Croux's Q_n and S_n, and Gini's mean difference (GMD), we quantified EEG features including power, connectivity, phase-amplitude coupling, entropy, and fractal dimension (which are commonly used to measure brain development) based on their inter-individual variabilities. Results from 51 infants (mean ± SD [range]: 193.17± 8.3 [180–209] days of age) revealed that the complexity measures, including fractal dimension and entropy, has the less variability across individuals. This stability was found to be significant in the right parieto-temporal region for both measures. This study shed light on our understanding of physiological patterns of EEG data in developing brains and contributes to the establishment of robust EEG biomarkers of neurodevelopment through the application of variance analyses.

Group Authors

Saeideh Davoudi, Tyler Schwartz, Aurélie Labbe, Laurel Trainor, Sarah Lippé

Author

• Saeideh Davoudi, University of Montreal
  Presenting Author