Identifying EEG Biomarkers As Potential Change Indicators in Autism Spectrum Disorder Clinical Studies

Friday, May 12, 2017: 12:00 PM-1:40 PM
Golden Gate Ballroom (Marriott Marquis Hotel)
N. V. Manyakov1, G. Pandina2, S. Ness3, A. Bangerter4, D. Lewin3, S. Jagannatha3, M. Boice2, A. Skalkin3, W. Cioccia5, M. S. Goodwin6, R. Hendren7, B. L. Leventhal8, F. Shic9 and G. Dawson10, (1)Computational Biology, Janssen Research & Development, LLC, Beerse, Belgium, (2)Janssen Research & Development, Titusville, NJ, (3)Janssen Research & Development, LLC, Titusville, NJ, (4)Janssen Research & Development, LLC, Pennington, NJ, (5)Janssen, Long Valley, NJ, (6)Northeastern University, Boston, MA, (7)University of California San Francisco, San Francisco, CA, (8)UCSF, San Francisco, CA, (9)Center for Child Health, Behavior and Development, Seattle Children's, Seattle, WA, (10)Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC
Background: Autism spectrum disorder (ASD) is a neurological condition, thus it is expected that correlates of brain functioning can discriminate between ASD and typically developing (TD) populations, and furthermore these EEG-derived measures may account for variation in core and associated symptoms of ASD. Based on existing research, abnormal hemispherical asymmetry and differences in attentional allocation may be detected using EEG in ASD. Moreover, GABAergic dysfunction may lead to associated changes in related frequency bands. Identification of relationships between EEG correlates of brain activity and reported symptoms can contribute to the development of potential targets and novel outcome measures.

Objectives: Identify differences between TD and ASD in EEG recordings and relation of these measures to symptom severity.

Methods: JAKESense – a set of biosensors and experimental tasks – was administered to individuals with ASD (n=127, mean [SD] age: 14.6 [7.91]) and TD participants (n=41, mean [SD] age: 16.3 [13.18]). EEG was recorded according to 10-20 system during resting state condition (eyes closed or viewing hourglass), social vs non-social videos, and point-light displays of biological vs non-biological motions. Experiments were performed at 9 different geographical locations, ranging from research sites professional with EEG to clinical sites with no prior EEG experience. EEG was recorded simultaneously with eye-tracking, and only data, when participants were looking at stimuli, where considered for analysis. Age and gender was considered as covariates during analysis.

Results: During resting state, significantly (p<0.05) smaller alpha power at posterior regions were found in ASD in comparison to TD participants primary during eyes closed condition. Posterior alpha power correlated negatively, while frontal gamma correlated positively with symptom severity (e.g. occipital, eyes closed α vs ABC lethargy social withdrawal r=-0.4, p=0.001, frontal gamma vs SRS total r=0.35, p=0.011). Ratio in power between left and right hemispheres, characterizing functional brain asymmetry, were found significantly (p<0.05) smaller in ASD than TD for eyes opened and closed conditions for many brain regions and frequency bands. Coherence between frontal and other regions was significantly (p<0.05) higher in delta, theta and alpha and smaller in gamma in ASD than TD. During observation of social videos, suppression in alpha in comparison to non-social videos was significantly more prominent in TD than ASD in frontal, central and temporal regions (p<0.01), while no significant difference in theta ratio between two conditions of videos was found. Ratio in frontal alpha between biological and non-biological motion conditions correlated positively with social communication skills (e.g., r=0.23, p=0.073 at Fz)

Conclusions: EEG measures show potential for discriminating between ASD and TD individuals, and correlate with core and associated ASD symptoms. Difficulties obtaining reliable EEG recordings across a number of novice sites suggest that careful training and recording processes are needed, as well as use of stimuli that is engaging and reduces participant discomfort. Such instruction, processes, and stimuli have been established throughout these observational studies to enhance future use of EEG and enable continued understanding of this biosensor’s contribution to the development of sensitive outcome measures in ASD.