Applied Intelligence

, Volume 49, Issue 5, pp 1748–1770 | Cite as

Dynamic brain functional parcellation via sliding window and artificial bee colony algorithm

  • Xuewu Zhao
  • Junzhong JiEmail author
  • Xing Wang


Dynamic brain functional parcellation is an important way to reveal the dynamics of brain function. However, current dynamic brain functional parcellation methods can not meet the need to clearly understand the dynamics. This paper presents a dynamic brain functional parcellation method based on sliding window and artificial bee colony (ABC) algorithm (called SWABC). In SWABC, a functional connectivity similarity minimum criterion (FCSMC) is firstly developed for determining the length of a sliding window and functional connectivity matrices are calculated with Pearson correlation and windowed time series of voxels. Then, an improved ABC is employed to identify functional states through clustering these matrices, where a hybrid search strategy and a dynamic radius constraint are respectively designed for employed bee search and scout bee search to enhance the search capability of ABC. Next, functional connectivity between voxels in each functional state is computed by concatenating time series belonging to the same state, and functional parcellation results for all functional states are achieved by performing the improved ABC. Finally, with comparison to other four algorithms, the experimental results on fMRI data of posterior cingulate cortex show that SWABC not only has better search capability, but also can yield reasonable functional states and corresponding functional parcellation results with stronger functional consistency and regional continuity. Moreover, the rationality of functional parcellation results from SWABC is also verified by functional connectivity fingerprints of subregions in each of them.


Dynamic brain functional parcellation Sliding window Artificial bee colony (ABC) algorithm Functional connectivity similarity minimum criterion (FCSMC) Dynamic radius constraint 



The work is partly supported by the NSFC Research Program (61672065), Henan Science and Technology Project (142102210588).


  1. 1.
    Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD (2014) Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex 24(3):663–676CrossRefGoogle Scholar
  2. 2.
    Arslan S, Ktena SI, Makropoulos A, Robinson EC, Rueckert D, Parisot S (2018) Human brain mapping: a systematic comparison of parcellation methods for the human cerebral cortex. NeuroImage 170:5–30CrossRefGoogle Scholar
  3. 3.
    Balsters JH, Mantini D, Wenderoth N (2018) Connectivity-based parcellation reveals distinct cortico-striatal connectivity fingerprints in autism spectrum disorder. NeuroImage 170:412–423CrossRefGoogle Scholar
  4. 4.
    Chen S, Ji B, Li Z, Langley J, Hu X (2016) Dynamic analysis of resting state fmri data and its applications. In: 2016 IEEE international conference on Acoustics, speech and signal processing (ICASSP). IEEE, pp 6295–6299Google Scholar
  5. 5.
    Chen X, Zhang H, Zhang L, Shen C, Lee S, Shen D (2017) Extraction of dynamic functional connectivity from brain grey matter and white matter for mci classification. Hum Brain Mapp 38(10):5019–5034CrossRefGoogle Scholar
  6. 6.
    Cheng H, Song D, Wu H, Fan Y (2012) Intrinsic functional connectivity pattern-based brain parcellation using normalized cut. In: Medical imaging 2012: Image processing. International society for optics and photonics, vol 8314, pp 83144fGoogle Scholar
  7. 7.
    Cheng H, Wu H, Fan Y (2014) Optimizing affinity measures for parcellating brain structures based on resting state fmri data: a validation on medial superior frontal cortex. J Neurosci Methods 237:90–102CrossRefGoogle Scholar
  8. 8.
    Cohen AL, Fair DA, Dosenbach NU, Miezin FM, Dierker D, Van Essen DC, Schlaggar BL, Petersen SE (2008) Defining functional areas in individual human brains using resting functional connectivity mri. Neuroimage 41(1):45–57CrossRefGoogle Scholar
  9. 9.
    Craddock RC, James GA, Holtzheimer PE, Hu X, Mayberg HS (2012) A whole brain fmri atlas generated via spatially constrained spectral clustering. Hum Brain Mapp 33(8):1914–1928CrossRefGoogle Scholar
  10. 10.
    Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1 (2):224–227CrossRefGoogle Scholar
  11. 11.
    Friston KJ (2011) Functional and effective connectivity in neuroimaging: a synthesis. Hum Brain Mapp 1 (1):13–36MathSciNetGoogle Scholar
  12. 12.
    Gilmore AW, Nelson SM, Chen HY, Mcdermott KB (2018) Task-related and resting-state fmri identify distinct networks that preferentially support remembering the past and imagining the future. Neuropsychologia 110:180–189CrossRefGoogle Scholar
  13. 13.
    Glasser MF, Coalson TS, Robinson EC, Hacker CD, Harwell J, Yacoub E, Ugurbil K, Andersson J, Beckmann CF, Jenkinson M (2016) A multi-modal parcellation of human cerebral cortex. Nature 536 (7615):171–178CrossRefGoogle Scholar
  14. 14.
    Inkaya T, Kayalıgil S, Özdemirel NE (2016) Swarm intelligence-based clustering algorithms: A survey. In: Unsupervised learning algorithms, pp 303–341Google Scholar
  15. 15.
    Ji B, Li Z, Li K, Li L, Langley J, Shen H, Nie S, Zhang R, Hu X (2016) Dynamic thalamus parcellation from resting-state fmri data. Hum Brain Mapp 37(3):954–967CrossRefGoogle Scholar
  16. 16.
    Kafashan M, Bj PA, Ching S (2018) Dimensionality reduction impedes the extraction of dynamic functional connectivity states from fmri recordings of resting wakefulness. J Neurosci Methods 293:151–161CrossRefGoogle Scholar
  17. 17.
    Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report, Technical report-tr06, Erciyes University, engineering faculty computer engineering departmentGoogle Scholar
  18. 18.
    Karaboga D, Ozturk C (2011) A novel clustering approach: Artificial bee colony (abc) algorithm. Appl Soft Comput J 11(1):652–657CrossRefGoogle Scholar
  19. 19.
    Katanoda K, Matsuda Y, Sugishita M (2002) A spatio-temporal regression model for the analysis of functional mri data. Neuroimage 17(3):1415–1428CrossRefGoogle Scholar
  20. 20.
    Leonardi N, Van De Ville D (2015) On spurious and real fluctuations of dynamic functional connectivity during rest. Neuroimage 104:430–436CrossRefGoogle Scholar
  21. 21.
    Moghimi P, Lim KO, Netoff TI (2017) Construction and evaluation of hiera6rchical parcellation of the brain using fmri with prewhitening. arXiv:1712.08180
  22. 22.
    Shakil S, Lee CH, Keilholz SD (2016) Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states. Neuroimage 133:111–128CrossRefGoogle Scholar
  23. 23.
    Spreng RN, Mar RA, Kim AS (2009) The common neural basis of autobiographical memory, prospection, navigation, theory of mind, and the default mode: a quantitative meta-analysis. J Cogn Neurosci 21(3):489–510CrossRefGoogle Scholar
  24. 24.
    Taube W, Mouthon M, Leukel C, Hoogewoud HM, Annoni JM, Keller M (2015) Brain activity during observation and motor imagery of different balance tasks: an fmri study. Cortex 64:102–114CrossRefGoogle Scholar
  25. 25.
    Tejwani R, Liska A, You H, Reinen J, Das P (2017) Autism classification using brain functional connectivity dynamics and machine learning. arXiv:1712.08041
  26. 26.
    Vogt BA, Laureys S (2005) Posterior cingulate, precuneal and retrosplenial cortices: cytology and components of the neural network correlates of consciousness. Prog Brain Res 150(2):205–217CrossRefGoogle Scholar
  27. 27.
    Vogt BA, Vogt L, Laureys S (2006) Cytology and functionally correlated circuits of human posterior cingulate areas. Neuroimage 29(2):452–466CrossRefGoogle Scholar
  28. 28.
    Wang J, Ju L, Wang X (2009) An edge-weighted centroidal voronoi tessellation model for image segmentation. IEEE Trans Image Process 18(8):1844–1858MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Wijayanto AW, Purwarianti A, et al. (2016) Fuzzy geographically weighted clustering using artificial bee colony: an efficient geo-demographic analysis algorithm and applications to the analysis of crime behavior in population. Appl Intell 44(2):377–398CrossRefGoogle Scholar
  30. 30.
    Yang Z, Craddock RC, Margulies D, Yan CG, Milham MP (2014) Common intrinsic connectivity states among posteromedial cortex subdivisions: Insights from analysis of temporal dynamics. Neuroimage 93(2):124–137CrossRefGoogle Scholar
  31. 31.
    Zeng L, Hu D, Liu H (2013) Temporal dynamics of the spontaneous activity in the human brain revealed anti-correlated brain states. In: Sociaty for Neuroscience Annual Meeting, San Diego, USAGoogle Scholar
  32. 32.
    Zhang Y, Caspers S, Fan L, Fan Y, Song M, Liu C, Mo Y, Roski C, Eickhoff S, Amunts K (2015) Robust brain parcellation using sparse representation on resting-state fmri. Brain Struct Funct 220(6):3565–3579CrossRefGoogle Scholar
  33. 33.
    Zhao X, Ji J, Yao Y (2017) Insula functional parcellation from fmri data via improved artificial bee-colony clustering. In: International conference on brain informatics, pp 72–82Google Scholar
  34. 34.
    Zhao XW, Ji JZ, Liang PP (2016) The human brain functional parcellation based on fmri data (in chinese). Chin Sci Bull 61(18):2035–2052Google Scholar
  35. 35.
    Zuo Z, Sun Y, Humphreys GW, Song Y (2017) Different activity patterns for action and language within their shared neural areas: an fmri study on action observation and language phonology. Neuropsychologia 99:112–120CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information TechnologyBeijing University of TechnologyBeijingChina
  2. 2.College of SoftwareNanyang Normal UniversityNanyangChina

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