The Autism Biomarkers Consortium for Clinical Trials: EEG Interim Analyses

Oral Presentation
Saturday, May 12, 2018: 11:20 AM
Grote Zaal (de Doelen ICC Rotterdam)
S. J. Webb1, A. Naples2, A. R. Levin3, G. Hellemann4, C. Sugar5, D. Senturk4, M. Santhosh6, H. M. Borland7, T. McAllister2, S. Hasselmo2, F. Shic7, M. Murias8, J. Dziura9, C. Brandt9, R. Bernier1, K. Chawarska2, G. Dawson10, S. Faja11, S. Jeste5, C. A. Nelson11 and J. McPartland2, (1)Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, (2)Child Study Center, Yale University School of Medicine, New Haven, CT, (3)Neurology, Boston Children's Hospital, Boston, MA, (4)UCLA, Los Angeles, CA, (5)University of California, Los Angeles, Los Angeles, CA, (6)Seattle Children's Research Institute, Seattle, WA, (7)Center for Child Health, Behavior and Development, Seattle Children's Research Institute, Seattle, WA, (8)Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, (9)Yale University, New Haven, CT, (10)Department of Psychiatry and Behavioral Sciences, Duke Center for Autism and Brain Development, Durham, NC, (11)Boston Children's Hospital, Boston, MA
Background: Autism Biomarkers Consortium for Clinical Trials (ABC-CT) electroencephalography (EEG) paradigms aim to identify social-communicative brain biomarkers relevant to autism spectrum disorder (ASD), using measures of signal strength (i.e., modulation of EEG spectral power and event-related potential latency/amplitude). Prior to commencing the full scale “main” study data collection (ongoing), a feasibility study was conducted to ensure standardization and viability of data collection across sites and effectiveness of data processing, extraction, and quality control procedures. This is particularly vital for EEG, as there have been few multisite data collection studies or test-retest evaluations, specifically in this age range and including the full functional variability in ASD.

Objectives: To investigate the acquisition validity, test-retest reliability; discriminant validity, and relation to clinical status of EEG measures of social communication in ASD.

Methods: EEG was collected across 51 participants (25 with ASD, 26 typically developing (TD), ages 4-11) over two days counterbalanced by day and within-day experiment order: (A) Resting (or calm viewing), visual evoked potential (VEP; checkerboard), and EU-AIMS Faces (based on the EU-AIMS face experiment), (B) Resting, Biomotion (point light displays of walkers), Dynamic social/nonsocial videos, and Emotion Faces. Equipment (EGI 128-channel EEG system), experimental control, and recording parameters were standardized across sites. Data processing and analyses were conducted via NetStation and Matlab, utilizing automated artifact detection and custom-made programs for component abstraction. Primary dependent variables, selected based on literature review, included slope of the power spectrum (Resting, Dynamic social/nonsocial), which is thought to be a marker of signal:noise ratio within the brain, as well as amplitude and latency of the P1 (VEP) and N170 (Biomotion, Faces, Emotion Faces) components. Validity was defined as valid acquisition (e.g., participant protocol compliance) and artifact free data for >40 seconds or 30% of trials.

Results: During feasibility, validity rates were >90% for Resting, Dynamic, VEP, and EU-AIMS Faces, and >80% for Biomotion and Emotion Faces. Significant differences (ps<.05) were found for Group for Resting slope (t=9.4). Effects of age were identified in Resting slope (t=4.1), Emotion Faces N170 latency (t=5.2), and VEP P1 amplitude (t=2.9). Interactions were found for diagnosis by age for Faces N170 amplitude (t=1.7), and diagnosis by verbal IQ for Emotion N170 amplitude (t=5.2) and VEP P1 amplitude (t=2.7). This symposium will present updated data from interim analyses, which includes Ns>175 of the main study sample and includes Resting, VEP, Faces (modified from feasibility), and Biomotion. Current Main Study (baseline) rates of acquisition are 97.5% valid session acquisition with valid derived results between 81% and 94% by experiment.

Conclusions: EEG measures showed high levels of data retention across site, age, and diagnostic categories. Biomarkers were highly sensitive to age and showed high levels of construct validity. Interim analyses data will provide additional information regarding EEG biomarker performance in feasibility and main study, test-retest stability, and implications for identifying an EEG biomarker with suitable psychometric performance, relation to clinical status, and potential for stratification in future clinical trials.