29913
The Early Trajectory of Relative Power in Autism Spectrum Disorders

Poster Presentation
Thursday, May 2, 2019: 11:30 AM-1:30 PM
Room: 710 (Palais des congres de Montreal)
S. Huberty1, S. van Noordt2, J. Desjardins3, M. H. Johnson4, E. J. Jones5, C. A. Nelson6, H. Tager-Flusberg7, A. R. Levin8, J. Frohlich9, &. the BASIS Team10 and M. Elsabbagh11, (1)Mcgill University, Montreal, QC, Canada, (2)Montreal Neurological Institute, McGill University, Montreal, QC, Canada, (3)SHARCNET, St Catharines, ON, Canada, (4)Centre of Brain and Cognitive Development, Birkbeck College, University of London, London, United Kingdom, (5)Centre for Brain and Cognitive Development, Birkbeck, University of London, London, United Kingdom, (6)Boston Children's Hospital, Boston, MA, (7)Psychological and Brain Sciences, Boston University, Boston, MA, (8)Neurology, Boston Children's Hospital, Boston, MA, (9)University of California Los Angeles, Los Angeles, CA, (10)Centre for Brain and Cognitive Development, Birkbeck University of London, London, United Kingdom, (11)McGill University, Montreal, PQ, Canada
Background: During the last decade, there has been increased interest in studying infants who have an older sibling with ASD (infant-siblings). Given that that ~20% of these infants will be diagnosed with ASD, studying infant-siblings from early infancy may help identify early behavioral or Neural precursors of ASD. Previous studies have found that oscillatory power in early infancy is predictive of later language ability (Levin, 2017), and that infants at risk for ASD have significantly lower frontal absolute EEG power across multiple frequency bands at 6 months of age, compared to infants not at high risk for ASD (Tierney, 2012). However, these studies are often limited by relatively small samples sizes and little is known about the early trajectory of EEG power in infants who eventually receive a diagnosis of ASD.

Objectives: As part of the international infant EEG data integration platform (EEG-IP), combining over 400 participants from three infant-sibling EEG studies, we aim to study spectral relative EEG power within the first year of life as it relates to autism risk.

Methods: We used resting state EEG data from two infant-sibling studies (Boston, USA and London, UK) at 3,6,9,12,18,24, and 36 months of age. The Boston data set included data from 250 infants between 3-36 months, and London included data from 104 infants between 6 and 12 months. Data were processed using a lossless pipeline that employs robust measures to isolate systematic sources of artifact (e.g., unreliable channel signal, non-stationarity, movement, non-cortical biological sources), and Adaptive Mixture Independent Component Analysis (AMICA) to isolate spatially fixed field projections. The EEG was interpolated to 10-20 sites. Following Tierney et al., 2012, Relative power was extracted in delta (2-4 Hz), theta, (4-6 Hz) low alpha (6-9 Hz), high alpha (9-13 Hz), beta (13-30 Hz), and gamma (30-50 Hz) bands from and 9 regions corresponding to 10-20 channels F3,Fz,F4,C3,Cz,C4,P3,Pz,P4. Relative power values between high familial risk for ASD (HR) and low risk infants (LR) were compared at each visit.

Results: At 3 and 6 months, gamma was significantly higher in all regions in the HR group (p <.01). At 6 months, theta was significantly lower in the HR group (p <.01), as well as low alpha in Regions Cz,C4,P4 (p <.05). At 12 months differences in theta and gamma converge, while alpha remained lower in the HR group for regions Fz-F4-Pz. (p <.05). Despite no differences at 24 months, the HR group had lower gamma power and higher theta power at 18 months (p <.01) and higher gamma power at 36 months (p <.05).

Conclusions: Results suggest that differences between HR and LR infants are more pronounced at the edges of the frequency distribution in early infancy. In the coming months as more data are added to the repository, our analyses will focus on differentiating the subset of infants who developed ASD, and measure trajectories of change in oscillatory power across development for all groups.

See more of: Biomarkers (molecular, phenotypic, neurophysiological, etc)
See more of: Biomarkers (molecular, phenotypic, neurophysiological, etc)