Skip to main content

Small-world indices via network efficiency for brain networks from diffusion MRI

Abstract

The small-world architecture has gained considerable attention in anatomical brain connectivity studies. However, how to adequately quantify small-worldness in diffusion networks has remained a problem. We addressed the limits of small-world measures and defined new metric indices: the small-world efficiency (SWE) and the small-world angle (SWA), both based on the tradeoff between high global and local efficiency. To confirm the validity of the new indices, we examined the behavior of SWE and SWA of networks based on the Watts–Strogatz model as well as the diffusion tensor imaging (DTI) data from 75 healthy old subjects (aged 50–70). We found that SWE could classify the subjects into different age groups, and was correlated with individual performance on the WAIS-IV test. Moreover, to evaluate the sensitivity of the proposed measures to network, two network attack strategies were applied. Our results indicate that the new indices outperform their predecessors in the analysis of DTI data.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  • Akil H, Martone ME, Van Essen DC (2011) Challenges and opportunities in mining neuroscience data. Science 331(6018):708

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Andrews-Hanna JR, Snyder AZ, Vincent JL, Lustig C, Head D, Raichle ME, Buckner RL (2007) Disruption of large-scale brain systems in advanced aging. Neuron 56(5):924–935

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Ashburner J (2009) Computational anatomy with the SPM software. Magn Reson Imaging 27(8):1163–1174

    Article  PubMed  Google Scholar 

  • Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26(3):839–851

    Article  PubMed  Google Scholar 

  • Bai F, Shu N, Yuan Y, Shi Y, Yu H, Wu D et al (2012) Topologically convergent and divergent structural connectivity patterns between patients with remitted geriatric depression and amnestic mild cognitive impairment. J Neurosci 32(12):4307–4318

    CAS  Article  PubMed  Google Scholar 

  • Bartzokis G, Sultzer D, Lu PH, Nuechterlein KH, Mintz J, Cummings JL (2004) Heterogeneous age-related breakdown of white matter structural integrity: implications for cortical “disconnection” in aging and Alzheimer’s disease. Neurobiol Aging 25(7):843–851

    CAS  Article  PubMed  Google Scholar 

  • Bassett DS, Bullmore ED (2006) Small-world brain networks. Neurosci 12(6):512–523

    Google Scholar 

  • Bellec P, Benhajali Y, Carbonell F, Dansereau C, Albouy G, Pelland M, Orban P (2015) Impact of the resolution of brain parcels on connectome-wide association studies in fMRI. Neuroimage 123:212–228

    Article  PubMed  Google Scholar 

  • Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang DU (2006) Complex networks: Structure and dynamics. Phys Rep 424(4):175–308

    Article  Google Scholar 

  • Bortoletto M, Veniero D, Thut G, Miniussi C (2015) The contribution of TMS–EEG coregistration in the exploration of the human cortical connectome. Neurosci Biobehav Rev 49:114–124

    Article  PubMed  Google Scholar 

  • Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186

    CAS  Article  PubMed  Google Scholar 

  • Chen VCH, Shen CY, Liang SHY, Li ZH, Tyan YS, Liao YT et al (2016) Assessment of abnormal brain structures and networks in major depressive disorder using morphometric and connectome analyses. J Affect Disord 205:103–111

    Article  PubMed  Google Scholar 

  • Chhabra A, Thakkar RS, Andreisek G, Chalian M, Belzberg AJ, Blakeley J et al (2013) Anatomic MR imaging and functional diffusion tensor imaging of peripheral nerve tumors and tumor like conditions. Am J Neuroradiol 34(4):802–807

    CAS  Article  PubMed  Google Scholar 

  • Cui Z, Zhong S, Xu P, He Y, Gong G (2013) Panda: a pipeline toolbox for analyzing brain diffusion images. Front Hum Neurosci 7(42):42

    PubMed  PubMed Central  Google Scholar 

  • de Reus MA, Van den Heuvel MP (2013) The parcellation-based connectome: limitations and extensions. Neuroimage 80:397–404

    Article  PubMed  Google Scholar 

  • Delbeuck X, Van der Linden M, Collette F (2003) Alzheimer’disease as a disconnection syndrome? Neuropsychol Rev 13(2):79–92

    CAS  Article  PubMed  Google Scholar 

  • Gigandet X, Griffa A, Kober T, Daducci A, Gilbert G, Connelly A et al (2013) A connectome-based comparison of diffusion MRI schemes. PloS one 8(9):e75061

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Hagmann P, Kurant M, Gigandet X, Thiran P, Wedeen VJ, Meuli R, Thiran JP (2007) Mapping human whole-brain structural networks with diffusion MRI. PloS one 2(7):e597

    Article  PubMed  PubMed Central  Google Scholar 

  • Hagmann P, Sporns O, Madan N, Cammoun L, Pienaar R, Wedeen VJ,et al (2010) White matter maturation reshapes structural connectivity in the late developing human brain. Proc Natl Acad Sci 107(44):19067–19072

    CAS  Article  PubMed  Google Scholar 

  • Humphries MD, Gurney K, Prescott TJ (2006) The brainstem reticular formation is a small-world, not scale-free, network. Proc R Soc Lond B Biol Sci 273(1585):503–511

    CAS  Article  Google Scholar 

  • Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM (2012) FSL. Neuroimage 62(2):782–790

    Article  PubMed  Google Scholar 

  • Johansenberg H, Rushworth MF (2009) Using diffusion imaging to study human connectional anatomy. Annu Rev Neurosci 32(1):75

    CAS  Article  Google Scholar 

  • Jones DK (2010) Challenges and limitations of quantifying brain connectivity in vivo with diffusion MRI. Imaging Med 2(3):341

    Article  Google Scholar 

  • Latora V, Marchiori M (2001) Efficient behavior of small-world networks. Phys Rev Lett 87(19):198701

    CAS  Article  PubMed  Google Scholar 

  • Lin L, Jin C, Fu Z, Zhang B, Bin G, Wu S (2016) Predicting healthy older adult’s brain age based on structural connectivity networks using artificial neural networks. Comput Methods Progr Biomed 125:8–17

    Article  Google Scholar 

  • Liu Y, Duan Y, He Y, Wang J, Xia M, Yu C et al (2012) Altered topological organization of white matter structural networks in patients with neuromyelitis optica. PloS one 7(11):e48846

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • Lo CY, Wang PN, Chou KH, Wang J, He Y, Lin CP (2010) Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer’s disease. J Neurosci 30(50):16876–16885

    CAS  Article  PubMed  Google Scholar 

  • Long Z, Duan X, Wang Y, Liu F, Zeng L, Zhao JP, Chen H (2015) Disrupted structural connectivity network in treatment-naive depression. Prog Neuropsychopharmacol Biol Psychiatry 56:18–26

    Article  PubMed  Google Scholar 

  • Mori S, van Zijl P (2002) Fiber tracking: principles and strategies—a technical review. NMR Biomed 15(7–8):468–480

    Article  PubMed  Google Scholar 

  • O’Sullivan M, Jones DK, Summers PE, Morris RG, Williams SCR, Markus HS (2001) Evidence for cortical “disconnection” as a mechanism of age-related cognitive decline. Neurology 57(4):632–638

    Article  PubMed  Google Scholar 

  • Odish OF, Caeyenberghs K, Hosseini H, Van Den Bogaard SJ, Roos RA, Leemans A (2015) Dynamics of the connectome in Huntington’s disease: a longitudinal diffusion MRI study. NeuroImage Clinl 9:32–43

    Article  Google Scholar 

  • Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3):1059–1069

    Article  PubMed  Google Scholar 

  • Shu N, Liu Y, Li J, Li Y, Yu C, Jiang T (2009) Altered anatomical network in early blindness revealed by diffusion tensor tractography. PloS one, 4(9):e7228

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Simard D, Nadeau L, Kröger H (2004) Fastest learning in small-world neural networks. Phys Lett A 336(1):8–15

    Article  CAS  Google Scholar 

  • Sporns O (2012) From simple graphs to the connectome: networks in neuroimaging. Neuroimage 62(2):881–886

    Article  PubMed  Google Scholar 

  • Sporns O, Tononi G, Kötter R (2005) The human connectome: a structural description of the human brain. PLoS Comput Biol 1(4):e42

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Strogatz SH (2001) Exploring complex networks. Nature 410(6825):268

    CAS  Article  PubMed  Google Scholar 

  • Telesford QK, Joyce KE, Hayasaka S, Burdette JH, Laurienti PJ (2011) The ubiquity of small-world networks. Brain Connect 1(5):367

    Article  PubMed  PubMed Central  Google Scholar 

  • Teng X, Yong H (2012) Mapping the Alzheimer’s brain with connectomics. Front Psychiatry 2:77

    Google Scholar 

  • Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N et al (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1):273–289

    CAS  Article  PubMed  Google Scholar 

  • Vaessen MJ, Hofman PAM, Tijssen HN, Aldenkamp AP, Jansen JF, Backes WH (2010) The effect and reproducibility of different clinical DTI gradient sets on small world brain connectivity measures. Neuroimage 51(3):1106–1116

    CAS  Article  PubMed  Google Scholar 

  • van den Heuvel MP, Mandl RC, Stam CJ, Kahn RS, Pol HEH (2010) Aberrant frontal and temporal complex network structure in schizophrenia: a graph theoretical analysis. J Neurosci 30(47):15915–15926

    Article  CAS  PubMed  Google Scholar 

  • Wang B, Fan Y, Lu M, Li S, Song Z, Peng X et al (2013) Brain anatomical networks in world class gymnasts: a DTI tractography study. NeuroImage 65:476–487

    Article  PubMed  Google Scholar 

  • Wang T, Shi F, Jin Y, Yap PT, Wee CY, Zhang J et al (2016) Multilevel deficiency of white matter connectivity networks in alzheimer’s disease: a diffusion mri study with dti and hardi models. Neural Plast 2016(2):2947136

    PubMed  PubMed Central  Google Scholar 

  • Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440

    CAS  Article  Google Scholar 

  • Weiss LG, Saklofske DH, Coalson DL, Raiford SE (2010) WAIS-IV clinical use and interpretation. Academic Press, San Diego

    Google Scholar 

  • Zalesky A, Fornito A, Harding IH, Cocchi L, Yücel M, Pantelis C, Bullmore ET (2010) Whole-brain anatomical networks: does the choice of nodes matter? Neuroimage 50(3):970–983

    Article  PubMed  Google Scholar 

  • Zhao T, Cao M, Niu H, Zuo XN, Evans A, He Y et al (2015) Age-related changes in the topological organization of the white matter structural connectome across the human lifespan. Hum Brain Mapp 36(10):3777–3792

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers for their helpful suggestions and appreciate professor Zalesky from the University of Melbourne and Melbourne Health for sharing the uniform parcellation algorithm. This work was supported by Grants from the Scientific Research General Project of Beijing Municipal Education Committee (KM201810005033), the Natural Science Foundation of Beijing (7143171) and the National Key Technology Support Program of China (2015BAI02B03).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Lan Lin.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lin, L., Fu, Z., Jin, C. et al. Small-world indices via network efficiency for brain networks from diffusion MRI. Exp Brain Res 236, 2677–2689 (2018). https://doi.org/10.1007/s00221-018-5326-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00221-018-5326-z

Keywords

  • Small world
  • Connectome
  • DTI
  • Brain network