Skip to main content
SpringerLink
Log in

Resting-State Brain Activity Complexity in Early-Onset Schizophrenia Characterized by a Multi-scale Entropy Method

  • Conference paper
  • First Online:
Intelligence Science and Big Data Engineering (IScIDE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10559))

Abstract

Early-onset schizophrenia (EOS) is a severe mental illness associated with changes of brain’s activity. However, the complexities of brain activity in EOS are still lacking. To address this issue, a multi-scale sample entropy (MSE) method was used to investigate the role of brain signal complexity in EOS. We recruited 39 patients with EOS (age from 12 to 18), 31 age- and sex-matched healthy controls. Reduced blood-oxygen-level-dependent (BOLD) complexity was observed in the superior temporal sulcus and cuneus. Increased BOLD complexity was observed in the middle frontal gyrus, superior partial lobule, precuneus and cingulate gyrus. Furthermore, we found the complexity changes in cingulate gyrus were associated with clinical symptoms of schizophrenia. These results suggested that the changes of complexity are crucial to understand the pathomechanism of schizophrenia.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Rm, H., et al.: Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80(1), 360 (2013)

    Google Scholar 

  2. Damaraju, E., et al.: Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. Neuroimage Clin. 5, 298–308 (2014)

    Article  Google Scholar 

  3. Ma, S., et al.: Dynamic changes of spatial functional network connectivity in healthy individuals and schizophrenia patients using independent vector analysis. Neuroimage 90, 196–206 (2014)

    Article  Google Scholar 

  4. Callicott, J.H., et al.: Complexity of prefrontal cortical dysfunction in schizophrenia: more than up or down. Am. J. Psychiatry 160(160), 2209–2215 (2003)

    Article  Google Scholar 

  5. Koukkou, M., et al.: Dimensional complexity of EEG brain mechanisms in untreated schizophrenia. Biol. Psychiatry 33(6), 397–407 (1993)

    Article  Google Scholar 

  6. Takahashi, T., et al.: Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia: a multiscale entropy analysis. Neuroimage 51(1), 173–182 (2010)

    Article  Google Scholar 

  7. Epstein, K.A., et al.: White matter abnormalities and cognitive impairment in early-onset schizophrenia-spectrum disorders. J. Am. Acad. Child Adolesc. Psychiatry 53(3), 362–372 (2014)

    Article  Google Scholar 

  8. Douaud, G., et al.: Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia. Brain 130(9), 2375–2386 (2007)

    Article  Google Scholar 

  9. Cannon, M., et al.: Evidence for early-childhood, pan-developmental impairment specific to schizophreniform disorder: results from a longitudinal birth cohort. Arch. Gen. Psychiatry 59(5), 449–456 (2002)

    Article  MathSciNet  Google Scholar 

  10. Azami, H., et al.: Evaluation of resting-state magnetoencephalogram complexity in Alzheimer’s disease with multivariate multiscale permutation and sample entropies (2015)

    Google Scholar 

  11. Sokunbi, M.O., et al.: Resting state fMRI entropy probes complexity of brain activity in adults with ADHD. Psychiatry Res. 214(3), 341–348 (2013)

    Article  Google Scholar 

  12. Liu, C.Y., et al.: Complexity and synchronicity of resting state blood oxygenation level-dependent (BOLD) functional MRI in normal aging and cognitive decline. J. Magn. Reson. Imaging Jmri 38(1), 36 (2013)

    Article  Google Scholar 

  13. Lai, M.C., et al.: A shift to randomness of brain oscillations in people with autism. Biol. Psychiatry 68(68), 1092–1099 (2010)

    Article  Google Scholar 

  14. Yang, A.C., et al.: Decreased resting-state brain activity complexity in schizophrenia characterized by both increased regularity and randomness. Hum. Brain Mapp. 36(6), 2174–2186 (2015)

    Article  Google Scholar 

  15. Gómez, C., et al.: Complexity analysis of resting-state MEG activity in early-stage Parkinson’s disease patients. Ann. Biomed. Eng. 39(12), 2935–2944 (2011)

    Article  Google Scholar 

  16. Accardo, A., et al.: Use of the fractal dimension for the analysis of electroencephalographic time series. Biol. Cybern. 77(5), 339–350 (1997)

    Article  MATH  Google Scholar 

  17. Pincus, S.M., et al.: Hormone pulsatility discrimination via coarse and short time sampling. Am. J. Physiol. 277(5 Pt 1), E948–E957 (1999)

    Google Scholar 

  18. Fernández, A., et al.: Complexity and schizophrenia. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 45, 267–276 (2012)

    Article  Google Scholar 

  19. Marx, E., et al.: Eyes open and eyes closed as rest conditions: impact on brain activation patterns. Neuroimage 21(4), 1818–1824 (2004)

    Article  Google Scholar 

  20. Pang, Y., et al.: Extraversion modulates functional connectivity hubs of resting‐state brain networks. J. Neuropsycho. (2015)

    Google Scholar 

  21. Wei, L., et al.: Specific frequency bands of amplitude low-frequency oscillation encodes personality. Hum. Brain Mapp. 35(1), 331–339 (2014)

    Article  Google Scholar 

  22. Power, J.D., et al.: Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59(3), 2142–2154 (2012)

    Article  Google Scholar 

  23. Friston, K.J., et al.: Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapping 2(4), 189–210 (1994)

    Article  Google Scholar 

  24. Tomasi, D., Volkow, N.D.: Aging and functional brain networks. Mol. Psychiatry 17(5), 549–558 (2012)

    Article  Google Scholar 

  25. Fan, L., et al.: The human brainnetome atlas: a new brain atlas based on connectional architecture. Cereb. Cortex 26(8), 3508 (2016)

    Article  Google Scholar 

  26. Eickhoff, S.B., Constable, R.T., Yeo, B.T.: Topographic organization of the cerebral cortex and brain cartography. Neuroimage (2017)

    Google Scholar 

  27. Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol.-Heart Circ. Physiol. 278(6), H2039–H2049 (2000)

    Google Scholar 

  28. Bowden, N.A., Scott, R.J., Tooney, P.A.: Altered gene expression in the superior temporal gyrus in schizophrenia. BMC Genom. 9(1), 1–12 (2008)

    Article  Google Scholar 

  29. Kasai, K., et al.: Progressive decrease of left superior temporal gyrus gray matter volume in patients with first-episode schizophrenia. Am. J. Psychiatry 160(1), 156–164 (2003)

    Article  Google Scholar 

  30. Whitford, T.J., et al.: Structural abnormalities in the cuneus associated with Herpes Simplex Virus (type 1) infection in people at ultra high risk of developing psychosis. Schizophr. Res. 135(1–3), 175 (2012)

    Article  Google Scholar 

  31. Pearlson, G.D.: Superior temporal gyrus and planum temporale in schizophrenia: a selective review. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 21(8), 1203 (1997)

    Article  Google Scholar 

  32. Plaze, M., et al.: Left superior temporal gyrus activation during sentence perception negatively correlates with auditory hallucination severity in schizophrenia patients. Schizophr. Res. 87(1–3), 109–115 (2006)

    Article  Google Scholar 

  33. Barta, P.E., et al.: Auditory hallucinations and smaller superior temporal gyral volume in schizophrenia. Am. J. Psychiatry 147(11), 1457–1462 (1990)

    Article  Google Scholar 

  34. Camchong, J., et al.: Common neural circuitry supporting volitional saccades and its disruption in schizophrenia patients and relatives. Biol. Psychiatry 64(12), 1042–1050 (2008)

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank Yan Zhang, Jingping Zhao for fMRI data acquisition, as well as Shaoqiang Han and Huafu Chen for their assistance with the analyses.

Funding

The work was supported by the 863 project (2015AA020505), the Natural Science Foundation of China (61533006, 61673089) and Fundamental Research Funds for the Central Universities (ZYGX2016KYQD120, ZYGX2015J141).

Author information

Authors and Affiliations

  1. Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Xiao Wang, Shaoqiang Han & Huafu Chen

  2. Key Laboratory for Mental Health of Hunan Province, Mental Health Institute, The Second Xiangya Hospital of Central South University, Changsha, China

    Yan Zhang

  3. Mental Health Institute, The Second Xiangya Hospital of Central South University, 139, Middle Renmin Road, Changsha, 410011, Hunan, China

    Jingping Zhao

Authors
  1. Xiao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shaoqiang Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jingping Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Huafu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jingping Zhao or Huafu Chen .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Yi Sun

  2. Dalian University of Technology, Dalian, China

    Huchuan Lu

  3. Dalian University of Technology, Dalian, China

    Lihe Zhang

  4. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  5. Beijing Institute of Technology, Beijing, China

    Hua Huang

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Wang, X., Zhang, Y., Han, S., Zhao, J., Chen, H. (2017). Resting-State Brain Activity Complexity in Early-Onset Schizophrenia Characterized by a Multi-scale Entropy Method. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_52

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67777-4_52

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67776-7

  • Online ISBN: 978-3-319-67777-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation