Correlations between the EEG Spectral Characteristics and the Severity of Autistic Manifestations

Analyzing the brain bioelectrical activity by EEG at rest is a simple and accessible method of research in children. Spectral EEG analysis can identify areas where certain types of activity, that reflect the excitation and inhibition balance, are predominant. Research is complicated by the lack of biological tests for the diagnosis of autism, the illness is diagnosed by clinical characteristics only. Thus, the gold standard of diagnostics is ADOS-2, a technique that assesses the severity of social communication deficits and stereotypical behavior in a child by observing his/her play and interaction.

The aim of the study was to assess the EEG spectral characteristics for their correlation with the ADOS-2 score that reflects the severity of autistic manifestations.

Materials and Methods. Eighteen children with a confirmed diagnosis of “childhood autism” were examined using ADOS-2. To assess the severity of autism manifestations, we used the comparative ADOS-2 score. Resting state EEG was collected for all subjects with subsequent calculation of the relative spectral power.

Results. The examined children showed various degrees of autism, and their comparative ADOS-2 score ranged from 4 to 10. A positive correlation of the comparative score with the EEG spectral power at several leads was revealed. The best correlation was found in the beta range of EEG: the higher the beta activity level, the more pronounced the autistic manifestations. The result indicates that the imbalance between excitation and inhibition is gradually involved in the pathogenesis of autism. Therefore, the spectral power value in the beta range can be potentially used as a dynamic indicator of autism when behavioral changes are difficult to assess.

Conclusion. The relative EEG spectral power in the beta range positively correlates with the ADOS-2 comparative score and therefore can serve as a dynamic biomarker of autism.

1. Baio J., Wiggins L., Christensen D.L., Maenner M.J., Daniels J., Warren Z., Kurzius-Spencer M., Zahorodny W., Robinson Rosenberg C., White T., Durkin M.S., Imm P., Nikolaou L., Yeargin-Allsopp M., Lee L.C., Harrington R., Lopez M., Fitzgerald R.T., Hewitt A., Pettygrove S., Constantino J.N., Vehorn A., Shenouda J., Hall-Lande J., Van Naarden Braun K., Dowling N.F. Prevalence of autism spectrum disorder among children aged 8 years — autism and developmental disabilities monitoring network, 11 sites, United States, 2014. MMWR Surveill Summ 2018; 67(6): 1–23, https://doi.org/10.15585/mmwr.ss6706a1.
2. World Health Organization. Mezhdunarodnaya klassifikatsiya bolezney (10-y peresmotr) [International classification of diseases (10th revision)]. Saint Petersburg: Adis, 1994.
3. ICD-11: mortality and morbidity statistics. URL: https://icd.who.int.
4. Lai M.C., Lombardo M.V., Baron-Cohen S. Autism. Lancet 2014; 383(9920): 896–910, https://doi.org/10.1016/s0140-6736(13)61539-1.
5. Vorstman J.A.S., Parr J.R., Moreno-De-Luca D., Anney R.J.L., Nurnberger J.I. Jr., Hallmayer J.F. Autism genetics: opportunities and challenges for clinicaltranslation. Autism genetics: opportunities and challenges for clinical translation. Nat Rev Genet 2017; 18(6): 362–376, https://doi.org/10.1038/nrg.2017.4.
6. Rutter M., Le Couteur A., Lord C. ADI-R. Intervyu dlya diagnostiki autizma [Autism diagnostic interview-revised]. Pod. red. Sorokina A. [Sorokin A. (editor)]. Western Psychological Services; Giunti O.S.; 2014.
7. Lord C., Rutter M., DiLavore P., Risi S., Gotham K., Bishop S.L., Luyster R.D., Guthrie W. ADOS-2. Plan diagnosticheskogo obsledovaniya pri autizme [ADOS-2. Autism diagnostic observation schedule]. Western Psychological Services; Giunti O.S.; 2016.
8. Sorokin A.B., Davydova E.Y. Autism diagnostic evaluation schedule (ADOS-2) for evaluation of behavior and communication in toddlers with concern of autism spectrum disorder. Autizm i narusheniya razvitiya 2017; 15(2): 38–44, https://doi.org/10.17759/autdd.2017150204.
9. Gotham K., Pickles A., Lord C. Standardizing ADOS scores for a measure of severity in autism spectrum disorders. J Autism Dev Disord 2009; 39(5): 693–705, https://doi.org/10.1007/s10803-008-0674-3.
10. Heunis T.M., Aldrich C., de Vries P.J. Recent advances in resting-state electroencephalography biomarkers for autism spectrum disorder — а review of methodological and clinical challenges. Pediatr Neurol 2016; 61: 28–37, https://doi.org/10.1016/j.pediatrneurol.2016.03.010.
11. Yasuhara A. Correlation between EEG abnormalities and symptoms of autism spectrum disorder (ASD). Brain Dev 2010; 32(10): 791–798, https://doi.org/10.1016/j.braindev.2010.08.010.
12. Buckley A.W., Holmes G.L. Epilepsy and autism. Cold Spring Harb Perspect Med 2016; 6(4): a022749, https://doi.org/10.1101/cshperspect.a022749.
13. Brain electrical activity mapping for diagnosing psychiatric disorders: a review of the clinical evidence. Rapid response report: summary with critical appraisal. Ottawa (ON): Canadian Agency for Drugs and Technologies in Health; 2014.
14. Devitt N.M., Gallagher L., Reilly R.B. Autism spectrum disorder (ASD) and fragile X syndrome (FXS): two overlapping disorders reviewed through electroencephalography — what can be interpreted from the available information? Brain Sci 2015; 5(2): 92–117, https://doi.org/10.3390/brainsci5020092.
15. Coben R., Clarke A.R., Hudspeth W., Barry R.J. EEG power and coherence in autistic spectrum disorder. Clin Neurophysiol 2008; 119(5): 1002–1009, https://doi.org/10.1016/j.clinph.2008.01.013.
16. Pop-Jordanova N., Zorcec T., Demerdzieva A., Gucev Z. QEEG characteristics and spectrum weighted frequency for children diagnosed as autistic spectrum disorder. Nonlinear Biomed Phys 2010; 4(1): 4, https://doi.org/10.1186/1753-4631-4-4.
17. Shephard E., Tye C., Ashwood K.L., Azadi B., Asherson P., Bolton P.F., McLoughlin G. Resting-state neurophysiological activity patterns in young people with ASD, ADHD, and ASD + ADHD. J Autism Dev Disord 2018; 48(1): 110–122, https://doi.org/10.1007/s10803-017-3300-4.
18. van Diessen E., Senders J., Jansen F.E., Boersma M., Bruining H. Increased power of resting-state gamma oscillations in autism spectrum disorder detected by routine electroencephalography. Eur Arch Psychiatry Clin Neurosci 2015; 265(6): 537–540, https://doi.org/10.1007/s00406-014-0527-3.
19. Gurau O., Bosl W.J., Newton C.R. How useful is electroencephalography in the diagnosis of autism spectrum disorders and the delineation of subtypes: a systematic review. Front Psychiatry 2017; 8: 121, https://doi.org/10.3389/fpsyt.2017.00121.
20. Wang J., Barstein J., Ethridge L.E., Mosconi M.W., Takarae Y., Sweeney J.A. Resting state EEG abnormalities in autism spectrum disorders. J Neurodev Disord 2013; 5(1): 24, https://doi.org/10.1186/1866-1955-5-24.

Sorokin A.B., Balandina O.V., Polevaya S.A., Mishanov G.A., Savchuk L.V., Borzikov V.V., Dvoryaninova V.V.., Belova A.N. Correlations between the EEG Spectral Characteristics and the Severity of Autistic Manifestations. Sovremennye tehnologii v medicine 2019; 11(1): 84, https://doi.org/10.17691/stm2019.11.1.10