Skip to main content
Log in

Construction and Recognition of Functional Brain Network Model Based on Depression

  • Systems-Level Quality Improvement
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

In order to make a deep analysis of depression, the construction and recognition of functional brain network model based on depression is mainly studied. Firstly, the relevant information of the subjects is introduced in turn. The data of fMRI (functional magnetic resonance imaging) are pretreated, static and dynamic functional connections are analyzed, statistical analysis and testing are carried out, and then the results are analyzed and discussed. The results show that static functional connectivity provides a new research perspective and means for further understanding the connection patterns of brain functional networks in unipolar depression and bipolar depression. Dynamic functional connectivity analysis (DFA) extends the previous studies on unipolar depression and bipolar depression, and provides a potential biological marker for clinical identification of unipolar depression and bipolar depression.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Woo, C. W., Chang, L. J., Lindquist, M. A., and Wager, T. D., Building better biomarkers: Brain models in translational neuroimaging. Nat. Neurosci. 20(3):365, 2017.

    Article  CAS  Google Scholar 

  2. Bassett, D. S., and Sporns, O., Network neuroscience. Nat. Neurosci. 20(3):353, 2017.

    Article  CAS  Google Scholar 

  3. Redlich, R., Almeida, J. R., Grotegerd, D., Opel, N., Kugel, H., Heindel, W., and Dannlowski, U., Brain morphometric biomarkers distinguishing unipolar and bipolar depression: A voxel-based morphometry–pattern classification approach. JAMA Psychiatry 71(11):1222–1230, 2014.

    Article  Google Scholar 

  4. Su, J. P., Liu, H. F., Zhang, H. L., He, Y. J., and Nie, Y., Effects of different degrees of depression on inflammatory response and immune function in patients with ovarian cancer. J. Biol. Regul. Homeost. Agents 32(5):1225–1230, 2018.

    CAS  PubMed  Google Scholar 

  5. Di Stasio, D., Candotto, V., Serpico, R., Migliozzi, R., Petruzzi, M., Tammaro, M., Maio, C., Gritti, P., Lauritano, D., and Lucchese, A., Depression and distress in burning mouth syndrome: A case control study. J. Biol. Regul. Homeost. Agents 32(2 Suppl. 1):91–95, 2018.

    PubMed  Google Scholar 

  6. Torous, J., Staples, P., and Onnela, J. P., Realizing the potential of mobile mental health: New methods for new data in psychiatry. Curr. Psychiatry Rep. 17(8):61, 2015.

    Article  Google Scholar 

  7. Dai, Z., Yan, C., Li, K., Wang, Z., Wang, J., Cao, M., and He, Y., Identifying and mapping connectivity patterns of brain network hubs in Alzheimer's disease. Cereb. Cortex 25(10):3723–3742, 2014.

    Article  Google Scholar 

  8. Liu, F., Guo, W., Fouche, J. P., Wang, Y., Wang, W., Ding, J., and Chen, H., Multivariate classification of social anxiety disorder using whole brain functional connectivity. Brain Struct. Funct. 220(1):101–115, 2015.

    Article  CAS  Google Scholar 

  9. Calhoun, V. D., and Sui, J., Multimodal fusion of brain imaging data: A key to finding the missing link (s) in complex mental illness. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 1(3):230–244, 2016.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Guiling Li.

Ethics declarations

Conflict of Interest

Author Lin Wen declares that he has no conflict of interest. Author Shan Liu declares that he has no conflict of interest. Author Yurong Cao declares that he has no conflict of interest. Author Guiling Li declares that he has no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Systems-Level Quality Improvement

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wen, L., Liu, S., Cao, Y. et al. Construction and Recognition of Functional Brain Network Model Based on Depression. J Med Syst 43, 236 (2019). https://doi.org/10.1007/s10916-019-1198-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-019-1198-4

Keywords

Navigation