Brain Topography

, Volume 30, Issue 6, pp 810–821 | Cite as

Long-Range Temporal Correlations Reflect Treatment Response in the Electroencephalogram of Patients with Infantile Spasms

  • Rachel J. Smith
  • Amanda Sugijoto
  • Neggy Rismanchi
  • Shaun A. Hussain
  • Daniel W. Shrey
  • Beth A. Lopour
Original Paper


Infantile spasms syndrome is an epileptic encephalopathy in which prompt diagnosis and treatment initiation are critical to therapeutic response. Diagnosis of the disease heavily depends on the identification of characteristic electroencephalographic (EEG) patterns, including hypsarrhythmia. However, visual assessment of the presence and characteristics of hypsarrhythmia is challenging because multiple variants of the pattern exist, leading to poor inter-rater reliability. We investigated whether a quantitative measurement of the control of neural synchrony in the EEGs of infantile spasms patients could be used to reliably distinguish the presence of hypsarrhythmia and indicate successful treatment outcomes. We used autocorrelation and Detrended Fluctuation Analysis (DFA) to measure the strength of long-range temporal correlations in 21 infantile spasms patients before and after treatment and 21 control subjects. The strength of long-range temporal correlations was significantly lower in patients with hypsarrhythmia than control patients, indicating decreased control of neural synchrony. There was no difference between patients without hypsarrhythmia and control patients. Further, the presence of hypsarrhythmia could be classified based on the DFA exponent and intercept with 92% accuracy using a support vector machine. Successful treatment was marked by a larger increase in the DFA exponent compared to those in which spasms persisted. These results suggest that the strength of long-range temporal correlations is a marker of pathological cortical activity that correlates with treatment response. Combined with current clinical measures, this quantitative tool has the potential to aid objective identification of hypsarrhythmia and assessment of treatment efficacy to inform clinical decision-making.


Detrended fluctuation analysis Pediatric epilepsy West Syndrome Hypsarrhythmia Network Synchrony 



The authors would like to thank Dr. Ramesh Srinivasan for his critical review of this manuscript.


This study was funded in part by an Institute of Clinical and Translational Sciences UC Irvine-Children’s Hospital of Orange County Collaborative Grant and a Children’s Hospital of Orange County Pediatric Subspecialty Faculty Tithe Grant.

Compliance with Ethical Standards

Conflict of interest

None of the authors have potential conflicts of interest to be disclosed.

Supplementary material

10548_2017_588_MOESM1_ESM.eps (1.8 mb)
Supplementary Fig. 1 Treatment response vectors with both DFA exponent and DFA intercept as parameters. For each patient, the vector originates at the pre-treatment DFA exponent and intercept and ends at the post-treatment values. The magenta vectors represent non-responders and black vectors represent responders. Results are shown for the a delta band, b theta band, c alpha band, and d beta band (EPS 1866 KB)
10548_2017_588_MOESM2_ESM.eps (1.7 mb)
Supplementary Fig. 2 DFA exponent does not correlate with control subject age. Pearson correlations between the DFA exponent and subject age were not significant in the a delta band (p = 0.4911), b theta band (p=0.0644), c alpha band (p=0.2830), and d beta band (p=0.5971) (EPS 1786 KB)


  1. Babloyantz A, Destexhe A (1986) Low-dimensional chaos in an instance of epilepsy. Neurobiology 83:3513–3517Google Scholar
  2. Bardet JM, Kammoun I (2008) Asymptotic properties of the detrended fluctuation analysis of long range dependent processes. IEEE Trans Inf Theor 54:2041–2052CrossRefGoogle Scholar
  3. Bryce RM, Sprague KB (2012) Revisiting detrended fluctuation analysis. Sci Rep 2:315. doi: 10.1038/srep00315 CrossRefPubMedPubMedCentralGoogle Scholar
  4. Chen Z, Ivanov PC, Hu K, Stanley HE (2001) Effect of nonstationarities on detrended fluctuation analysis. Phys Rev E 64:111–114. doi: 10.1103/PhysRevE.64.011114 Google Scholar
  5. Ebersole JS, Pedley TA (2003) Current practice of clinical electroencephalography, 3rd edn. Lippincott Williams & Wilkins, PhiladelphiaGoogle Scholar
  6. Ferree TC, Hwa RC (2003) Power-law scaling in human EEG: relation to Fourier power spectrum. Neurocomputing 52:755–761CrossRefGoogle Scholar
  7. Hardstone R, Poil SS, Schiavone G et al (2012) Detrended fluctuation analysis: a scale-free view on neuronal oscillations. Front Physiol 3:75–87. doi: 10.3389/fphys.2012.00450 CrossRefGoogle Scholar
  8. Hooshmand H, Morganroth R, Corredor C (1980) Significance of focal and lateralized beta activity in the EEG. Clin Electroencephalogr 11:140–144. doi: 10.1177/155005948001100308 CrossRefPubMedGoogle Scholar
  9. Hrachovy R a, Frost JD, Kellaway P (1984) Hypsarrhythmia: variations on the theme. Epilepsia 25:317–325CrossRefPubMedGoogle Scholar
  10. Hussain SA, Kwong G, Millichap JJ et al (2015) Hypsarrhythmia assessment exhibits poor interrater reliability: a threat to clinical trial validity. Epilepsia 56:77–81. doi: 10.1111/epi.12861 CrossRefPubMedGoogle Scholar
  11. Hwa RC, Ferree TC (2004) Stroke detection based on the scaling properties of human EEG. Phys A 338:246–254CrossRefGoogle Scholar
  12. Japaridze N, Muthuraman M, Moeller F et al (2013) Neuronal networks in west syndrome as revealed by source analysis and renormalized partial directed coherence. Brain Topogr 26:157–170. doi: 10.1007/s10548-012-0245-y CrossRefPubMedGoogle Scholar
  13. Kannathal N, Chee J, Er K et al (2014) Chaotic Analysis of Epileptic EEG Signals. In: Goh J (ed) The 15th International Conference on Biomedical Engineering. Springer International Publishing, Geneva, pp 652–654CrossRefGoogle Scholar
  14. Kantelhardt JW, Koscielny-Bunde E, Rego HH et al (2001) Detecting long-range correlations with detrended fluctuation analysis. Phys A Stat Mech Appl 295:441–454. doi: 10.1016/S0378-4371(01)00144-3 CrossRefGoogle Scholar
  15. Kobayashi K, Akiyama T, Oka M et al (2015) A storm of fast (40–150 Hz) oscillations during hypsarrhythmia in West syndrome. Ann Neurol 77:58–67. doi: 10.1002/ana.24299 CrossRefPubMedGoogle Scholar
  16. Linkenkaer-Hansen K, Nikouline VV, Palva JM, Ilmoniemi RJ (2001) Long-range temporal correlations and scaling behavior in human brain oscillations. J Neurosci 21:1370–1377. doi: 10.1002/anie.201106423 PubMedGoogle Scholar
  17. Liu A, Hahn JS, Heldt GP, Coen RW (1992) Detection of neonatal seizures through computerized EEG analysis. Electroencephalogr Clin Neurophysiol 82:30–37. doi: 10.1016/0013-4694(92)90179-L CrossRefPubMedGoogle Scholar
  18. Lux AL, Osborne JP (2004) A proposal for case definitions and outcome measures in studies of infantile spasms and West syndrome: consensus statement of the West Delphi Group. Epilepsia 45:1416–1428. doi: 10.1111/j.0013-9580.2004.02404.x CrossRefPubMedGoogle Scholar
  19. Monto S, Vanhatalo S, Holmes MD, Palva JM (2007) Epileptogenic neocortical networks are revealed by abnormal temporal dynamics in seizure-free subdural EEG. Cereb Cortex 17:1386–1393. doi: 10.1093/cercor/bhl049 CrossRefPubMedGoogle Scholar
  20. Parish LM, Worrell GA, Cranstoun SD et al (2004) Long-range temporal correlations in epileptogenic and non-epileptogenic human hippocampus. Neuroscience 125:1069–1076. doi: 10.1016/j.neuroscience.2004.03.002 CrossRefPubMedGoogle Scholar
  21. Pavone P, Striano P, Falsaperla R et al (2013) Infantile spasms syndrome, West syndrome and related phenotypes: what we know in 2013. Brain Develop 36:739–751. doi: 10.1016/j.braindev.2013.10.008 CrossRefGoogle Scholar
  22. Riikonen RS (2010) Favourable prognostic factors with infantile spasms. Eur J Paediatr Neurol 14:13–18. doi: 10.1016/j.ejpn.2009.03.004 CrossRefPubMedGoogle Scholar
  23. Siniatchkin M, Van Baalen A, Jacobs J et al (2007) Different neuronal networks are associated with spikes and slow activity in hypsarrhythmia. Epilepsia 48:2312–2321. doi: 10.1111/j.1528-1167.2007.01195.x PubMedGoogle Scholar
  24. Smit DJA, de Geus EJC, van de Nieuwenhuijzen ME et al (2011) Scale-free modulation of resting-state neuronal oscillations reflects prolonged brain maturation in humans. J Neurosci 31:13128–13136. doi: 10.1523/JNEUROSCI.1678-11.2011 CrossRefPubMedGoogle Scholar
  25. Stadnitski T (2012) Measuring fractality. Front Physiol. doi: 10.3389/fphys.2012.00127 PubMedPubMedCentralGoogle Scholar
  26. Stamps F, Gibbs E, Rosenthal I, Gibbs F (1959) Treatment of hypsarrhythmia with ACTH. JAMA 171:116–119Google Scholar
  27. Sue WC, Mikati MA, Kramer U (1997) Hypsarrhythmia: Frequency and variant patterns and correlation with etiology and outcome. Neurology 48:197–203CrossRefPubMedGoogle Scholar
  28. Van Putten MJAM, Stam CJ (2001) Is the EEG really “chaotic” in hypsarrhythmia? IEEE Eng Med Biol Mag 20:72–79. doi: 10.1109/51.956822 CrossRefPubMedGoogle Scholar
  29. Wu JY, Koh S, Sankar R, Mathern GW (2008) Paroxysmal fast activity: an interictal scalp EEG marker of epileptogenesis in children. Epilepsy Res 82:99–106. doi: 10.1016/j.eplepsyres.2008.07.010 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Rachel J. Smith
    • 1
  • Amanda Sugijoto
    • 1
  • Neggy Rismanchi
    • 2
  • Shaun A. Hussain
    • 4
  • Daniel W. Shrey
    • 2
    • 3
  • Beth A. Lopour
    • 1
  1. 1.Department of Biomedical Engineering, The Henry Samueli School of EngineeringUniversity of CaliforniaIrvineUSA
  2. 2.Division of NeurologyChildren’s Hospital Orange CountyOrangeUSA
  3. 3.Department of PediatricsUniversity of CaliforniaIrvineUSA
  4. 4.Division of Pediatric NeurologyUniversity of CaliforniaLos AngelesUSA

Personalised recommendations