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Artificial bee colony clustering with self-adaptive crossover and stepwise search for brain functional parcellation in fMRI data

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Abstract

The emergence of functional magnetic resonance imaging (fMRI) provides a good opportunity for brain functional parcellation. However, the high dimension and low signal-to-noise ratio of fMRI data brings difficulties to the existing parcellation methods. To address the issue, this paper presents a novel brain functional parcellation method based on artificial bee colony clustering (ABCC) algorithm with self-adaptive crossover and stepwise search (called CSABCC). In CSABCC, the preprocessed fMRI data is first mapped into a low-dimensional space by spectral mapping to reduce its dimension and each food source position is encoded as a clustering solution composed of cluster centers. Then, CSABCC utilizes an improved artificial bee colony search procedure with some robustness advantage to seek better food sources, where a self-adaptive crossover is employed to enhance information exchange between individuals and onlooker bees adopt a stepwise search to improve its search capability. Finally, a functional parcellation result is obtained by mapping cluster labels onto the corresponding voxels. The experiments on simulated fMRI data show that CSABCC can generate the parcellation closest to the real result, and these results on real insula fMRI data also demonstrate that CSABCC has better search capability and can produce parcellation structures with stronger functional consistency and regional continuity compared to some other typical algorithms. Moreover, the correctness of the parcellation results is also validated by functional connectivity fingerprints of the corresponding subregions.

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Notes

  1. http://www.yonghelab.org/downloads/data.

  2. http://rfmri.org/DPARSF.

References

  • Arslan S, Rueckert D (2015) Multi-level parcellation of the cerebral cortex using resting-state fMRI. In: International conference on medical image computing and computer-assisted intervention, Springer, pp 47–54

  • Arslan S, Ktena SI, Makropoulos A, Robinson EC, Rueckert D, Parisot S (2017) Human brain mapping: a systematic comparison of parcellation methods for the human cerebral cortex. Neuroimage 170:5–30

    Article  Google Scholar 

  • Balsters JH, Mantini D, Wenderoth N (2018) Connectivity-based parcellation reveals distinct cortico-striatal connectivity fingerprints in autism spectrum disorder. NeuroImage 170:412–423

    Article  Google Scholar 

  • Baumgartner R, Scarth G, Teichtmeister C, Somorjai R, Moser E (1997) Fuzzy clustering of gradientecho functional mri in the human visual cortex. Part I: reproducibility. J Magn Reson Imaging 7(6):1094–1101

    Article  Google Scholar 

  • Blumensath T, Behrens TE, Smith SM (2012) Resting-state fMRI single subject cortical parcellation based on region growing. In: International conference on medical image computing and computer-assisted intervention, Springer, pp 188–195

  • Blumensath T, Jbabdi S, Glasser MF, Van Essen DC, Ugurbil K, Behrens TE, Smith SM (2013) Spatially constrained hierarchical parcellation of the brain with resting-state fMRI. Neuroimage 76(1):313–324

    Article  Google Scholar 

  • Cauda F, Costa T, Torta DM, Sacco K, D’Agata F, Duca S, Geminiani G, Fox PT, Vercelli A (2012) Meta-analytic clustering of the insular cortex: characterizing the meta-analytic connectivity of the insula when involved in active tasks. Neuroimage 62(1):343–355

    Article  Google Scholar 

  • Cha J, Jo HJ, Gibson WS, Lee JM (2017) Functional organization of the human posterior cingulate cortex, revealed by multiple connectivity-based parcellation methods. Hum Brain Mapp 38(6):2808–2818

    Article  Google Scholar 

  • Chang LJ, Yarkoni T, Khaw MW, Sanfey AG (2013) Decoding the role of the insula in human cognition: functional parcellation and large-scale reverse inference. Cereb Cortex 23(3):739–749

    Article  Google Scholar 

  • Chuang YC, Chen CT, Hwang C (2015) A real-coded genetic algorithm with a direction-based crossover operator. Inf Sci 305:320–348

    Article  Google Scholar 

  • Craddock RC, James GA, Rd HP, Hu XP, Mayberg HS (2012) A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum Brain Mapp 33(8):1914–1928

    Article  Google Scholar 

  • Deen B, Pitskel NB, Pelphrey KA (2011) Three systems of insular functional connectivity identified with cluster analysis. Cereb Cortex 21(7):1498–506

    Article  Google Scholar 

  • Dornas JV, Braun J (2018) Finer parcellation reveals detailed correlational structure of resting-state fMRI signals. J Neurosci Methods 294:15–33

    Article  Google Scholar 

  • Duval ER, Joshi SA, Block SR, Abelson JL, Liberzon I (2018) Insula activation is modulated by attention shifting in social anxiety disorder. J Anxiety Disord 56:56–62

    Article  Google Scholar 

  • Fathy YY, de Jong FJ, van Dam AM, Rozemuller AJ, van de Berg WD (2017) Insular cortex sub-region-dependent distribution pattern of \(\alpha \)-synuclein immunoreactivity in parkinson’s disease and dementia with lewy bodies. bioRxiv pp 1569–1584

  • Genon S, Reid A, Li H, Fan L, Müller VI, Cieslik EC, Hoffstaedter F, Langner R, Grefkes C, Laird AR et al (2017) The heterogeneity of the left dorsal premotor cortex evidenced by multimodal connectivity-based parcellation and functional characterization. NeuroImage 170:400–411

    Article  Google Scholar 

  • Hale JR, Mayhew SD, Mullinger KJ, Wilson RS, Arvanitis TN, Francis ST, Bagshaw AP (2015) Comparison of functional thalamic segmentation from seed-based analysis and ICA. NeuroImage 114:448–465

    Article  Google Scholar 

  • Hassanpour MS, Simmons WK, Feinstein JS, Luo Q, Lapidus RC, Bodurka J, Paulus MP, Khalsa SS (2018) The insular cortex dynamically maps changes in cardiorespiratory interoception. Neuropsychopharmacology 43(2):426–434

    Article  Google Scholar 

  • Honnorat N, Eavani H, Satterthwaite TD, Gur RE, Gur RC, Davatzikos C (2015) GraSP: geodesic graph-based segmentation with shape priors for the functional parcellation of the cortex. Neuroimage 106:207–221

    Article  Google Scholar 

  • Ilango SS, Vimal S, Kaliappan M, Subbulakshmi P (2018) Optimization using artificial bee colony based clustering approach for big data. Clust Comput 4:1–9

    Google Scholar 

  • Inkaya T, Kayaligil S, Ozdemirel NE (2016) Swarm intelligence-based clustering algorithms: a survey. In: Celebi M, Aydin K (eds) Unsupervised Learning Algorithms. Springer, Cham, pp 303–341

    Chapter  Google Scholar 

  • James GA, Hazaroglu O, Bush KA (2016) A human brain atlas derived via n-cut parcellation of resting-state and task-based fMRI data. Magn Reson Imaging 34(2):209–218

    Article  Google Scholar 

  • Janssen RJ, Jylnki P, Kessels RP, van Gerven MA (2015) Probabilistic model-based functional parcellation reveals a robust, fine-grained subdivision of the striatum. Neuroimage 119:398–405

    Article  Google Scholar 

  • Jiang L, Xu T, He Y, Hou XH, Wang J, Cao XY, Wei GX, Yang Z, He Y, Zuo XN (2015) Toward neurobiological characterization of functional homogeneity in the human cortex: regional variation, morphological association and functional covariance network organization. Brain Struct Funct 220(5):2485–2507

    Article  Google Scholar 

  • Jung WH, Jang JH, Park JW, Kim E, Goo EH, Im OS, Kwon JS (2014) Unravelling the intrinsic functional organization of the human striatum: a parcellation and connectivity study based on resting-state fMRI. PlOS ONE 9(9):e106,768

    Article  Google Scholar 

  • Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Tech. rep., Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department

  • Katanoda K, Matsuda Y, Sugishita M (2002) A spatio-temporal regression model for the analysis of functional MRI data. Neuroimage 17(3):1415–1428

    Article  Google Scholar 

  • Liu C, Abu-Jamous B, Brattico E, Nandi AK (2016) Towards tunable consensus clustering for studying functional brain connectivity during affective processing. Int J Neural Syst 27(2):1–16

    Google Scholar 

  • Liu F, Gao J, Di N, Adler LS (2015) Nectar attracts foraging honey bees with components of their queen pheromones. J Chem Ecol 41(11):1028–1036

    Article  Google Scholar 

  • Liu X, Chen X, Zheng W, Xia M, Han Y, Song H, Li K, He Y, Wang Z (2018) Altered functional connectivity of insular subregions in alzheimers disease. Front Aging Neurosci 10:107–118

    Article  Google Scholar 

  • Maggioni E, Tana MG, Arrigoni F, Zucca C, Bianchi AM (2014) Constructing fmri connectivity networks: a whole brain functional parcellation method for node definition. J Neurosci Methods 228(10):86–99

    Article  Google Scholar 

  • Mann PS, Singh S (2017) Artificial bee colony metaheuristic for energy-efficient clustering and routing in wireless sensor networks. Soft Comput 21(22):6699–6712

    Article  Google Scholar 

  • Mejia AF, Nebel MB, Shou H, Crainiceanu CM, Pekar JJ, Mostofsky S, Caffo B, Lindquist MA (2015) Improving reliability of subject-level resting-state fmri parcellation with shrinkage estimators. NeuroImage 112:14–29

    Article  Google Scholar 

  • Mishra A, Rogers BP, Li MC, Gore JC (2014) Functional connectivity-based parcellation of amygdala using self-organized mapping: a data driven approach. Hum Brain Mapp 35(4):1247–1260

    Article  Google Scholar 

  • Nebel MB, Joel SE, Muschelli J, Barber AD, Caffo BS, Pekar JJ, Mostofsky SH (2014) Disruption of functional organization within the primary motor cortex in children with autism. Hum Brain Mapp 35(2):567–580

    Article  Google Scholar 

  • Nomi JS, Farrant K, Damaraju E, Rachakonda S, Calhoun VD, Uddin LQ (2016) Dynamic functional network connectivity reveals unique and overlapping profiles of insula subdivisions. Hum Brain Mapp 37(5):1770–1787

    Article  Google Scholar 

  • Ogawa S, Lee TM, Kay AR, Tank DW (1990) Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci USA 87(24):9868–9872

    Article  Google Scholar 

  • Park By, Tark KJ, Shim WM, Park H (2018) Functional connectivity based parcellation of early visual cortices. Hum Brain Mapp 39(3):1380–1390

    Article  Google Scholar 

  • Peng X, Lin P, Wu X, Gong R, Yang R, Wang J (2017) Insular subdivisions functional connectivity dysfunction within major depressive disorder. J Affect Disord 227:280–288

    Article  Google Scholar 

  • Perri RL, Berchicci M, Bianco V, Spinelli D, Di Russo F (2018) Brain waves from an isolated cortex: contribution of the anterior insula to cognitive functions. Brain Struct Funct 223(3):1343–1355

    Google Scholar 

  • Plantinga BR, Temel Y, Duchin Y, Uludag K, Patriat R, Roebroeck A, Kuijf M, Jahanshahi A, Ter HRB, Vitek J (2016) Individualized parcellation of the subthalamic nucleus in patients with parkinson’s disease with 7T MRI. Neuroimage 168:403–411

    Article  Google Scholar 

  • Rausch A, Zhang W, Beckmann CF, Buitelaar JK, Groen WB, Haak KV (2018) Connectivity-based parcellation of the amygdala predicts social skills in adolescents with autism spectrum disorder. J Autism Dev Disord 48(2):572–582

    Article  Google Scholar 

  • Rge RE, Madsen KH, Schmidt MN, Mrup M (2017) Infinite von mises-fisher mixture modeling of whole brain fMRI data. Neural Comput 29(10):2712–2741

    Article  MathSciNet  MATH  Google Scholar 

  • Rogers-Carter MM, Varela JA, Gribbons KB, Pierce AF, McGoey MT, Ritchey M, Christianson JP (2018) Insular cortex mediates approach and avoidance responses to social affective stimuli. Nat Neurosci 21(3):404–414

    Article  Google Scholar 

  • Ryali S, Chen T, Supekar K, Menon V (2013) A parcellation scheme based on von Mises-Fisher distributions and Markov random fields for segmenting brain regions using resting-state fMRI. Neuroimage 65(1):83–96

    Article  Google Scholar 

  • Sahoo G (2017) A two-step artificial bee colony algorithm for clustering. Neural Comput Appl 28(3):537–551

  • Taherkhani M, Safabakhsh R (2016) A novel stability-based adaptive inertia weight for particle swarm optimization. Appl Soft Comput 38:281–295

    Article  Google Scholar 

  • Van Oort ES, Mennes M, Schröder TN, Kumar VJ, Jimenez NIZ, Grodd W, Doeller CF, Beckmann CF (2017) Functional parcellation using time courses of instantaneous connectivity. NeuroImage 170:30–41

  • Vercelli U, Diano M, Costa T, Nani A, Duca S, Geminiani G, Vercelli A, Cauda F (2016) Node detection using high-dimensional fuzzy parcellation applied to the insular cortex. Neural Plast 5–6:1–8

    Article  Google Scholar 

  • Wang Q, Chen R, Jaja J, Jin Y, Hong LE, Herskovits EH (2016) Connectivity-based brain parcellation. Neuroinformatics 14(1):83–97

    Article  Google Scholar 

  • Wig GS, Laumann TO, Cohen AL, Power JD, Nelson SM, Glasser MF, Miezin FM, Snyder AZ, Schlaggar BL, Petersen SE (2014) Parcellating an individual subject’s cortical and subcortical brain structures using snowball sampling of resting-state correlations. Cereb Cortex 24(8):2036–2054

    Article  Google Scholar 

  • Yamada T, Itahashi T, Nakamura M, Watanabe H, Kuroda M, Ohta H, Kanai C, Kato N, Hashimoto R (2016) Altered functional organization within the insular cortex in adult males with high-functioning autism spectrum disorder: evidence from connectivity-based parcellation. Mol Autism 7(1):41–55

    Article  Google Scholar 

  • Zaman M, Elsayed SM, Ray T, Sarker RA (2016) Evolutionary algorithms for dynamic economic dispatch problems. IEEE Trans Power Syst 31(2):1486–1495

    Article  Google Scholar 

  • 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):3565–3579

    Article  Google Scholar 

  • Zhao XW, Ji JZ, Liang PP (2016) The human brain functional parcellation based on fmri data (in chinese). Chin Sci Bull 61(18):2035–2052

    Article  Google Scholar 

  • Zhao XW, Ji JZ, Yao Y (2017) Insula functional parcellation by searching gaussian mixture model using immune clonal selection algorithm. J Zhejiang Univ (Eng Sci) 51(12):2320–2331

    Google Scholar 

  • Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173

    MathSciNet  MATH  Google Scholar 

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Acknowledgements

The work is partly supported by the NSFC Research Program (61672065, 61375059), the scientific and technological project in Henan Province of China (142102210588, 172102310702), and the Science and Technology Foundation of Henan Educational Committee of China (17A520049, 17A630046).

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Authors and Affiliations

  1. Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing University of Technology, Beijing, China

    Xuewu Zhao & Junzhong Ji

  2. College of Software, Nanyang Normal University, Nanyang, China

    Xuewu Zhao

  3. Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, USA

    Aidong Zhang

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  1. Xuewu Zhao
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Correspondence to Junzhong Ji.

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Zhao, X., Ji, J. & Zhang, A. Artificial bee colony clustering with self-adaptive crossover and stepwise search for brain functional parcellation in fMRI data. Soft Comput 23, 8689–8709 (2019). https://doi.org/10.1007/s00500-018-3467-4

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